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Physiological and Psychological Changes at the End of the Soccer Season in Elite Female Athletes

  • Universitat Ramon Llull, Barcelona. Spain


This study compares and describes relationships among stress-recovery indices, the heart rate variability index, and the Cooper and Yo-Yo IR1 tests among female soccer players during the last six weeks of the competitive season. Sixteen female soccer players engaged in a pre-test of all of the variables. After having their training monitored for six weeks, a post-test was administered. The results revealed significant (p < 0.05) differences in the specific stress-recovery scales of the RESTQ-sport and in the frequency-domain variables of the HRV, although there were no significant differences in the general stress or general recovery scales. The Yo-Yo IR1 test, the Cooper test scores, and the means of the time-domain HRV variables did not exhibit any significant differences between the pre- and the post-test. The RMSSD variations exhibited very large and large correlations with the performance test and the RESTQ-sport variables, respectively. The variations in the HRV frequency-domain variables exhibited significant moderate and large correlations among the variations of the RESTQ-sport scales. Monitoring athletes at the end of the season may reveal contradictions between some variables. To help with the interpretation of these scales, some external aspects, such as athlete strain and monotony of training, should be considered.
Journal of Human Kinetics volume 66/2019, 99-109 DOI: 10.2478/hukin-2018-0051 99
Section II Exercise Physiology & Sports Medicine
1 - Faculty of Psychology, Education Sciences and Sport Blanquerna–Ramon Llull University, Barcelona, Spain.
2 - Institut Francesc Macià. Departament d'ensenyament. Generalitat de Catalunya. Spain.
3 - Department of physiology, University of Barcelona, Barcelona, Spain.
Authors submitted their contribution to the article to the editorial board.
Accepted for printing in the Journal of Human Kinetics vol. 66/2019 in March 2019.
Physiological and Psychological Changes at the End
of the Soccer Season in Elite Female Athletes
Jose Morales1, Vicenç Roman2, Alexandre Yáñez1, Mònica Solana-Tramunt1,
Juan Álamo3, Antón Fíguls2
This study compares and describes relationships among stress-recovery indices, the heart rate variability index,
and the Cooper and Yo-Yo IR1 tests among female soccer players during the last six weeks of the competitive season.
Sixteen female soccer players engaged in a pre-test of all of the variables. After having their training monitored for six
weeks, a post-test was administered. The results revealed significant (p < 0.05) differences in the specific stress-recovery
scales of the RESTQ-sport and in the frequency-domain variables of the HRV, although there were no significant
differences in the general stress or general recovery scales. The Yo-Yo IR1 test, the Cooper test scores, and the means of
the time-domain HRV variables did not exhibit any significant differences between the pre- and the post-test. The RMSSD
variations exhibited very large and large correlations with the performance test and the RESTQ-sport variables,
respectively. The variations in the HRV frequency-domain variables exhibited significant moderate and large correlations
among the variations of the RESTQ-sport scales. Monitoring athletes at the end of the season may reveal contradictions
between some variables. To help with the interpretation of these scales, some external aspects, such as athlete strain and
monotony of training, should be considered.
Key words: RESTQ-sport, HRV, Yo-Yo IR1, Cooper test, monotony.
The competitive season in soccer is
characterized by different performance states of
players due to the application of different training
loads (TLs) that affect the stress-recovery (SR)
status of each athlete (Dupont et al., 2004; Flatt et
al., 2017). According to Thorpe et al. (2015), TL
patterns vary considerably between the playing
season and the preseason because of differences in
training and match volume. However, to the best
of our knowledge the number of studies involving
monitoring TLs and SR status of female soccer
players using various tools simultaneously in-
season is much smaller than the number of studies
focusing on male soccer players, and smaller than
those concerning athletes participating in other
sports. It is accepted that the final period of the
season is decisive for evaluating the final success of
a soccer team. In this sense, the control of TLs and
their psycho-physiological effects during this
period has an impact on players’ performance, and
knowledge of these data provides valuable
information to optimize the training process
(Borresen and Lambert, 2009).
Studies of team sports, especially soccer,
have been characterized by attempts to quantify
training and competition loads using different
systems. Nowadays, there are different options for
TL assessment during a soccer match, including
the GPS which allows researchers to assess several
TL indices (Buchheit et al., 2014; Malone et al.,
2015), heart rate (HR) monitors, blood lactate or gas
exchange measurements (Borresen and Lambert,
100 Physiological and psychological changes at the end of the soccer season in elite female athletes
Journal of Human Kinetics - volume 66/2019
2009), and other inexpensive and easy-to-apply
options such as the Rate of Perceived Exertion
(RPE) Session (RPEsession) (Foster et al., 1995) that
have been used in previous studies focusing on
soccer players (Algrøy et al., 2011).
Heart rate variability (HRV)
measurements have been used to measure internal
TLs and overtraining in soccer (Flatt et al., 2017).
Heart rate variability refers to variations in beat
intervals or correspondingly in the instantaneous
HR in different situations. Various studies have
used HRV to better understand the status of the
Autonomic Nervous System (ANS) and stress
induced by TLs; this stress interferes with the
sympathetic-parasympathetic ANS balance. The
relationship between ANS and HRV differs during
activity and immediate recovery situations
compared with during recovery periods
(Kaikkonen et al., 2007). Accurate HRV
assessments require stationary conditions;
therefore, it is preferable to measure HRV a
significant amount of time before or after exercise
or during immediate recovery states (Kaikkonen et
al., 2007; Task Force of the European Society of
Cardiology and the North American Society of
Pacing and Electrophysiology, 1996).
From a psychological perspective,
different instruments are useful for assessing
stress-recovery situations and internal TLs during
training and these instruments can be also used to
detect a possible overtraining situation or to
prevent an injury. Kellmann and Kallus (2001)
developed the Recovery Stress Questionnaire for
Athletes (RESTQ-sport), an instrument to measure
SR balance in athletes. The RESTQ-sport has been
used in soccer (Laux et al., 2015), and its
application is important for both competitions and
training sessions because it enables evaluations of
the current situation and expectations of
performance in upcoming games or training
Since performance in soccer depends on
many factors, the use of a variety of psychological
and physiological variables, together with a
detailed description of the characteristics of
training, is justified. Therefore, the objectives of
this study were: (i) to describe the TL of the last
mesocycle of the season, (ii) to compare the results
of performance, psychological and physiological
tests before and after the final mesocycle, and (iii)
to establish a relationship between the chosen
variables of the different domains evaluated. We
hypothesized that there would be significant
differences between the initial and the final values
due to the accumulated effect of training.
Sixteen professional female soccer players
from the Club de Fútbol Levante-Las Planas
(Barcelona) competing in the Spanish First
Division (age: 23.25 ± 5.07 years; body mass: 60.94
± 9.55 kg, body height: 1.61 ± 6.89 m, and body
mass index: 23.55 ± 2.11 kg/m2) participated in the
After being fully informed verbally and in
writing of the purposes and potential risks of the
study, the subjects provided their written consent
to participate in the investigation. The study and
its protocol were reviewed and approved by the
Ramon Llull University internal review board and
conducted in accordance with the latest version of
the Declaration of Helsinki.
Design and Procedures
We collected data during pre-test and post-
test sessions, just after Easter Week holidays. The
data collection occurred one week immediately
after the second mesocycle of the season. Between
tests, the soccer players continued with their last
mesocycle training program, and the re-test was
performed before the last week of the competitive
All testing sessions were completed 2 hours
prior to the start of training. During the testing
sessions, we measured HRV and collected RESTQ-
sport questionnaire information. The subjects also
performed the Cooper test and, after 48 hours, the
Yo-Yo IR1 test. The researchers responsible for
data collection were blinded to the participants
and the information was codified and statistically
treated by other researchers.
Six players were excluded to avoid HRV
disturbances because they were in their luteal
menstrual period; this phase is known to affect
HRV results (de Zambotti et al., 2013).
HRV analysis
In order to obtain accurate data, we applied
The Task Force of the European Society of
Cardiology and the North American Society of
Pacing and Electrophysiology criteria to all of the
HRV measurements. The HRV was always
recorded at 5 pm (2 hours before training), and the
by Jose Morales et al. 101
© Editorial Committee of Journal of Human Kinetics
athletes were instructed to avoid any stimulants,
thermogenics and alcohol for at least 48 hours. The
testing room conditions were standardized (quiet,
no distractions, temperature of 20-22°C).
The subjects were asked to remain still and
breathe at a pace between 10-12 cycles per minute
without speaking or making any movements.
These conditions were maintained during the pre-
and the post-test. The female athletes were fitted
with HR monitors and given 5 minutes to rest in a
supine position prior to the data collection.
Interbeat (RR) interval recordings were obtained
during the next 5 minutes of each subject using a
validated portable HR monitor (Polar® RS810,
Kempele, Finland). This series of HR monitors had
been validated for HRV recordings (Giles et al.,
2016). The RR recordings were downloaded using
the accompanying Polar software (Polar® Precision
Performance) and exported for later analysis of
time- and frequency-domain measures of HRV
using Kubios v2.0 HRV software (Biosignal
Analysis and Medical Imaging Group at the
Department of Applied Physics, University of
Kuopio, Kuopio, Finland).
Based on the original RR intervals, the
following variables were calculated: the standard
deviation of the RR-intervals (SDNN), the root-
mean-square successive difference of intervals
We analyzed different HRV variables in the
frequency domain, which were obtained by
analyzing the spectral analysis density of the RR
signal. As recommended by the Task Force (Task
Force of the European Society of Cardiology and
the North American Society of Pacing and
Electrophysiology 1996), we performed spectral
analysis with a Fast Fourier Transformer to
quantify the power spectral density of the low-
frequency (LF; 0.04–0.15 Hz) and high-frequency
(HF; 0.16–0.40 Hz) bands.
The HF band reflects vagal modulation, and
the LF band reflects both sympathetic and
parasympathetic influences. Furthermore, the
LF/HF ratio was examined as an indicator of
sympatho-vagal balance (Bosquet et al., 2008).
RESTQ-sport test analysis
The RESTQ-sport recovery-stress
questionnaire was used (Kellmann and Kallus,
2001) to determine the perceived stress of seasonal
The soccer players had to answer 77 questions
related to 19 different scales. In addition, the
RESTQ-sport scores were classified as general
stress scores (mean of 7 scales), general recovery
scores (mean of 5 scales), sport-specific stress
scores (mean of 3 scales), and sport-specific
recovery scores (mean of 4 scales). The final score
of each scale was the mean of the points obtained
in each item. Each subject’s answer was checked
based on a Likert scale with values ranging from 0–
The Spanish version of the RESTQ-sport was
used, which had been validated by Gonzalez-Boto
et al. (2009). This version demonstrated high
reliability with a Cronbach Alpha index ranging
from 0.77 to 0.94 and significant correlations with
the Profile of Mood States questionnaire and the
RESTQ-sport scales.
Performance tests
The Cooper test was conducted taking into
account the aspects described by McArdle et al.
(2010) (i.e., a 200-m track with markings every 50
m). All the subjects were familiarized with the test
protocol, and they performed a warm up of 2 min
of running and 3 min of stretching. To begin the
test, a "GO" command was given and the timer was
started. During the test, athletes were kept
informed of the remaining time at the end of each
lap (200 m). Once the test ended after 12 minutes,
the distance that the athlete covered, rounded to
the nearest 50 m mark, was registered.
The Yo-Yo IR1 test was performed following
the protocol proposed by Bangsbo et al. (2008) after
completion of a specific warm-up. The Yo-Yo IR1
test consisted of repeated 20-m runs back and forth
between two markers with a progressive increase
in speed, which was governed by an audio player.
Between each 40-m run, the athlete recovered with
10 s of jogging (shuttle runs of 2×5 m). The test
consisted of 4 runs at 10–13 km·h-1 (0–160 m) and
another 7 runs at 13.5–14.0 km·h-1 (160–440 m).
From this point on, the players completed 8
additional runs, each 0.5 km·h-1 faster than the
previous one. The test was completed when the
athlete reached voluntary exhaustion or failed to
maintain her running pace in synchrony with the
audio recording (i.e., she failed to achieve the
markers twice in the same stage). The Yo-Yo IR1
course lines were marked with cones 20 m
apart with additional cones set 5 m behind the
starting line for use during recovery.
The athletes wore HR monitors to record the
102 Physiological and psychological changes at the end of the soccer season in elite female athletes
Journal of Human Kinetics - volume 66/2019
final HR attained during the test.
Training program characteristics
The athletes underwent the same training
program for 6 weeks and focused on specific
objectives of the end of the competitive period. The
TL was characterized by using specific high-
intensity and decreased volume loads. The
recovery time was sufficient to ensure that the
athletes were fit during competition (Figure 1).
We monitored internal TLs using the HR-
based approach of Edwards (1993) and the RPE-
based approach of Foster et al. (2001).
The method of Edwards (1993) is based on the
time accumulated in five HR zones and is
calculated analyzing each zone (zone 1: 50–60% of
HRmax; zone 2: 60–70% of HRmax; zone 3: 70–80% of
HRmax; zone 4: 80–90% of HRmax; zone 5: 90–100% of
HRmax). The calculation was made by summing the
number of minutes in each zone multiplied by the
zone number. The HR assessment was made using
a portable HR monitor (Polar® RS810), and the
recordings were downloaded using the
accompanying Polar® software.
The method proposed by Foster et al. ( 2001)
requires subjects to rate the intensity of training 30
min after the end of the session using the Borg’s
Category Ratio-10 (CR-10) RPE scale. The TL is
calculated by multiplying the duration of exercise
by the RPEsession score.
We collected RPEsession scores and the scores of
the Edwards method and calculated the mean of
the week-long values of all of the subjects. At the
same time, the week-long monotony was
calculated according to Foster (1998) by dividing
the mean of the week-long TL from RPEsession by its
standard deviation. The monotony is a
measurement of variability of training across one
week. If the TL is always high or always low
during the week, the monotony value will be high.
However, if high and low TLs are intercalated
during the training week, the monotony score will
be moderate or low. The training strain is a value
that represents the stress to which the athlete was
exposed during the entire training week. It is
obtained by multiplying the weekly TL (including
the load of the competition) by the training
monotony score. A week of higher TLs along with
a high monotony score yields very large training
effort values.
Statistical analysis
We performed statistical analyses using the
Statistical Package for Social Science version 22.0
(SPSS, Inc., Chicago, IL, USA). A significance level
of p < 0.05 was used for all the tests. The Shapiro-
Wilk test was applied to test for a normal
distribution of the data. A repeated-measures
ANOVA of internal TL variables was carried out to
check for differences during the six weeks of
training. We relied on a paired t-test to compare the
differences between the pre- and post-test scores
for all dependent variables (HRV, RESTQ-sport,
and the performance test). Moreover, Pearson
product-moment correlations were used to
quantify the relationships between pre- and post-
test changes in HRV, RESTQ-sport and
performance variables. Finally, we evaluated the
correlation of all the dependent variables in the
post-test with the final result of the monotony and
the training strain of the last week of training.
Training load variables
A main effect of the time factor was observed.
Significant differences in the variables quantifying
the TLs according to the mesocycle week and the
Edwards method (F(5,45) = 39.45, p < 0.05; η2p = 0.39)
and RPEsession (F(5,45) = 47.36, p < 0.05; η2p = 0.31)
were noted. Figure 1 shows the values of the
different training variables. For the variables that
did not fulfill sphericity based on the Mauchly test,
the degrees of freedom were adjusted using the
Huynh-Feldt method. The monotony (F(3.55,32.88) =
6.67, p <0.05; η2p = 0.29) and the training strain
(F(3.15,30.18) = 16.65, p < 0.05; η2p = 0.21) also revealed
significant differences between the different weeks
of training. Pairwise comparisons were performed
using the Bonferroni post hoc tests and are shown
in Figure 1.
HRV variables
The means of the HRV variables did not
exhibit any significant differences in either the time
domain or in the pre- or post-test. We found
significant differences only in the frequency
analyses that used normalized units. There was a
significant increase (p < 0.05) in LF (t15 = 4.19; p =
0.001; r = 0.73) and the LF/HF ratio (t15 = 4.4; p =
0.001; r = 0.75) and a significant decrease in HF (t15
= 4.23; p = 0.001; r = 0.74) between the pre- and the
post-test. Descriptive analyses are provided in
Table 1.
Stress/recovery variables
Contrast analysis was used to compare the
by Jose Morales et al. 103
© Editorial Committee of Journal of Human Kinetics
means of RESTQ-sport results and the stress-
recovery perception of the subjects. There were
significant differences in sport-specific stress (t15 = -
4.95; p = 0.001; r = 0.79) and sport-specific recovery
(t15 = -5.9; p = 0.001; r = 0.84) between the pre- and
the post-test scores. No significant differences were
observed in the general stress or the general
recovery scales (Figure 2).
Performance variables
There were no significant differences between
the means of the performance tests in the pre- and
the post-test scores. Both tests’ scores had
increased, yet not significant results in the Cooper
test (t15 = -2.08; p = 0.58) and the Yo-Yo IR1 test (t15 =
-1.73; p = 0.11) (Figure 3).
Correlations between dependent variables
A very large, significant correlation was
found between RMSSD and Yo-YoIR1 (r = 0.84;
p = 0.02) and the Cooper test (r = 0.78; p = 0.03).
RMSSD also exhibited a large and significant
correlation with the RESTQ-sport variables,
general stress (-0.61; p = 0.01), specific stress (r =
0.58; p = 0.01), general recovery (r = 0.64; p = 0.003),
and specific recovery (r = 0.50; p = 0.009). The
other HRV time-domain variables exhibited non-
significant, but moderate correlations with
the RESTQ-sport and the performance test
variables (r = 0.34–0.45; p > 0.05).
The variations in the HRV frequency-domain
variables exhibited some significant moderate and
large correlations among the variations of RESTQ-
sport, HF and specific recovery (r = 0.68; p =
0.007), HF and specific stress (r = 0.61; p = 0.02),
HF and general stress (r = 0.55; p = 0.02), LF/HF
and specific recovery (r = 0.48; p = 0.01), LF/HF
and specific stress (r = 0.31; p = 0.04), LF/HF and
general stress (r = 0.55; p = 0.04), LF and specific
stress (r = 0.38; p = 0.04). The remainder of the
variables did not exhibit any significant
correlations; all were small or moderate (r = 0.16–
0.36; p > 0.05).
Training monotony exhibited high and
significant correlations with some variables of
RESTQ-sport, specific stress (r = 0.71; p = 0.02) and
specific recovery (r = -0.66; p = 0.03). There were
moderate correlations with the variable HRV of the
frequency domain HF (r = 0.48), LF (r = 0.41) and
LF/HF (r = 0.45). The remaining HRV variables did
not exhibit any significant correlations and were
low or very low in value.
Training strain exhibited high and moderate
correlations with RMSSD (r = -0.70; p = 0.01), Yo-Yo
IR1 (r = 0.44), and the Cooper test (r = 0.41). The
remaining variables did not exhibit any significant
correlations and were low or very low in value.
Table 1
Heart rate and HRV variable comparisons between the pre- and post-test.
Variable Pretest Posttest
HR 58.25 (2.42) 58.11 (3.12)
Time-domain HRV
MeanRR (ms) 957.15 (42.94) 917.23 (64.04)
STDRR (ms) 65.96 (9.07) 96.03 (11.68)
MeanHR (1/min) 62.84 (2.46) 76.97 (11.08)
STDHR (1/min) 4.11 (0.35) 10.88 (2.4)
RMSSD (ms) 72.56 (14.36) 76.6 (9.11)
Frequency-domain HRV
LF(u.n.) 43.12 (3.77) 60.84 (3.63) *
HF(u.n.) 56.14 (3.72) 38.47 (3.63) *
LF/HF ratio 0.76 (0.13) 1.58 (0.22) *
The data are expressed as mean (standard error of the mean).
*Indicates a significant difference (p < 0.05) between the pre- and post-test.
104 Physiological and psychological changes at the end of the soccer season in elite female athletes
Journal of Human Kinetics - volume 66/2019
Figure 1
Quantification of TLs of the team's weekly mean. 1 Means significant
differences compared to week 1, 2 Means significant differences compared to week 2,
and 3Means significant differences compared to week 3.
Figure 2
Differences in RESTQ-sport scales (general stress scores, general recovery, sport-specific stress,
and sport-specific recovery). The columns represent the mean and the error bars represent
the standard error of the mean. *Indicates significant differences (p < 0.05).
by Jose Morales et al. 105
© Editorial Committee of Journal of Human Kinetics
Figure 3
Differences in performance tests (Cooper and Yo-Yo IR1). The columns represent the mean
and the error bars represent the standard error of the mean.
*Indicates significant differences (p < 0.05).
The results revealed significant changes
between the initial measurement and some of the
analyzed variables at the end of the female soccer
players’ season. These changes can be observed in
the differences between the RESQT-sport scores
and the HRV results at the beginning and the end
of the analyzed mesocycle. In contrast, there were
no differences between the pre-test and the post-
test in the Cooper test or the Yo-Yo IR1 results,
although the planning strategy was focused on
tapering at the end of the season to obtain peak
performance in the athletes. Therefore, our results
were only partially consistent with our initial
hypothesis because not all of the variables
exhibited significant differences between the pre-
and the post-test.
The coaches decided on a progressively
decreasing TL during the last 3 weeks of the
mesocycle. These changes, which consisted of a
decrease in intensity and an increase in the number
of tactical sessions, were consistent with the
training methods documented in other studies
that followed the same kind of a program to
improve performance after a tapering period
(Milanez et al., 2014). The TL assessment results
during the mesocycle were consistent with the
coaches’ strategies. Both the methods of Edwards
and RPEsession exhibited a progressive reduction
in the TL over the last 3 weeks. Furthermore, there
was a notable increase in monotony scores during
the last three weeks. At the same time, the training
strain did not decrease in the same proportion as
the TLs represented by the RPEsession and
Edwards indices. Previous studies have used
monotony scores with psychological and
physiological TL assessments (da Silva et al., 2015).
It ha s been reported than high monotony scores ar e
related to low TL variability, which may suggest
the beginning of overtraining (Foster, 1998). When
monotony and training strain were represented
together with the weekly TL (Figure 1), we could
observe that the sessions were not properly
managed, because a significant reduction in TLs
should result in a corresponding reduction in
monotony and training strain, which was not the
106 Physiological and psychological changes at the end of the soccer season in elite female athletes
Journal of Human Kinetics - volume 66/2019
case. The increased monotony was due to low
variety in training that could have been avoided by
alternating intensity and volume between
consecutive sessions (Comyns and Flanagan,
2013). The strong correlations found between the
monotony and the specific stress and recovery
indices of the RESTQ-sport can be attributed to the
TL being more affected by psychological than
physiological factors.
The performance test scores did not exhibit
significant differences (p > 0.05) between the pre-
and the post-test of the analyzed mesocycle. There
was a little improvement in the results, which was
far from what was expected after applying a
tapering strategy during the last weeks of the
season. These results are consistent with those of
Oliveira et al. (2013), in which the changes in Yo-
Yo test performance are evident during the pre-
season, but not during the season, despite training.
Changes in the physiological performance
test (Yo-YoIR1 and Cooper) during the last
mesocycle of the season only exhibited significant
and large correlations with RMSSD. This HRV
variable did not exhibit any significant differences
between the pre- and post-test period, and yielded
similar results as the other performance variables.
These findings are consistent with those of Flatt et
al. (2017).
Meanwhile, the variables of the frequency
domain of the HRV exhibited a significant
decrease. This finding could be interpreted as
showing that the team was coming to the end of the
season and beginning to enter a phase of
overtraining or that the players displayed
relatively high indices of stress, aspects that were
not reflected by the design of the loads or a
decrease in players’ performance. Other tennis
(Thiel et al., 2011) and women’s soccer (Flatt and
Esco, 2015) studies that included an intense
training period reported that lower HRV variables
were not associated with decreased performance.
Our results are similar to those of previous Yo-Yo
IR1 test results, and the Yo-Yo IR1 improvement
was not related to the relative changes in short-
term HRV. These changes could explain the
absence of a correlation with the HRV variables
(Buchheit et al., 2011; Oliveira et al., 2013).
However, the HRV does not have to reflect the
recovery of all systems since there are many
different stressors unrelated to training (e.g., social,
educational, occupational, economic, nutritional,
travel, and time-related stress) (Flatt et al., 2017;
Tian et al., 2013).
The use of the RESTQ-sport combined
with physiological, non-invasive outcome
measurements such as HRV has several precedents
for monitoring the effects of the TL simultaneously
with changes in stress and recovery (Dupuy et al.,
2013; Morales et al., 2014; Thiel et al., 2011).
Previous studies have reported similar results
when comparing stress and physiological
variables. For instance, Morales et al. ( 2014)
reported decreased HRV values with higher levels
of stress and lower recovery after 4 weeks of
intense training with judo athletes; performance
variables exhibited a significant decrease. Other
studies have found that HRV and RESTQ-sport
showed the same behavior, but did not correlate
with athletes’ performance (Dupuy et al., 2013;
Thiel et al., 2011).
In the present study, the general stress and
general recovery indices of the RESTQ-sport did
not exhibit significant differences. Nevertheless,
the sport-specific stress and sport-specific recovery
scales demonstrated significant differences (p <
0.05). These results might suggest overtraining,
although here this conclusion has been ruled out
due the results of the Cooper and Yo-Yo IR1 tests,
which indicated stable athletes’ performance.
These findings suggest that motivational factors or
training monotony likely resulted in the higher
sport-specific stress scores and lower sport-specific
recovery at the end of the season compared with
the baseline status. In this sense, the results
presented here are in agreement with those of
Thorpe et al. (2015). This author affirmed that
psychometric variables predicted fatigue in elite
soccer players better than other markers such as
HRV or Counter-Movement Jump.
The frequency-domain HRV results
exhibited significant differences between the pre-
and post-test, indicating decreased HRV and
higher sympathetic ANS activation in response to
the stressful situation (Stanley et al., 2013).
According to Bosquet et al. (2008), HRV measured
in the frequency domain is moderately reliable, but
requires a highly standardized protocol to control
for breathing, time of the day, temperature,
luminosity, and noise, among other
factors. Recent studies have considered time-
domain variables, like the log RMSSD, to be the
best indicator for athletic monitoring because this
by Jose Morales et al. 107
© Editorial Committee of Journal of Human Kinetics
variable appears to be uninfluenced by the
breathing rate and is more reliable than frequency-
domain variables (Plews et al., 2014).
Our study meets the Task Force-
standardized protocol requirements to control for
the influence of external factors (Task Force, 1996).
Even so, our results are controversial in the HRV
domain because the frequency domain exhibited
significant differences between the pre- and the
post-test and the time domain did not. The same
situation occurred in the RESTQ-sport results; only
the specific sport variables exhibited significant
differences. A possible explanation for these
results is that time-domain variables were more
sensitive to external factors because of the
increased anxiety produced by the overlap of the
school exam period with the end of the training
season and the associated decrease in motivation
(D’Ascenzi et al., 2013).
The influence of the female menstrual
cycle on HRV made it impossible to compare the
groups more than once per month. Moreover,
several previous studies have reported the effect of
the female period on physiological functions such
as HRV; sex-hormone fluctuations are basic
physiological factors that continuously affects
body functioning in women. Such studies have
found increased LF components accompanied or
unaccompanied by decreased HF components in
HRV during the luteal phase compared with the
follicular phase and indicated a predominant
sympathetic activity in the luteal phase and a
dominant vagal activity in the follicular phase (de
Zambotti et al., 2013). The majority of previous
studies based their results on analyses of
frequency-domain and non-linear HRV variables,
and it is rare to find studies that report the effect of
the female period on the time domain of HRV
variables. Recently, Brar et al. (2015) observed the
effect of the female period on HRV, and these
authors concluded that time-domain variables,
particularly the lnRMSSD, did not always exhibit
significant differences, in contrast with frequency-
domain variables. As a result, Brar et al. (2015)
were unable to easily detect period-induced
There are a number of limitations of this
study that should be considered. The most
important one pertains to the HRV data collection,
because the most recent studies (Flatt et al., 2017;
Flatt and Esco, 2015) recommend monitoring HRV
on a weekly basis by comparing the evolution of
the coefficient of variation of a single variable such
as lnRMSSD. In our study, the pre- and post-tests
were performed 6 weeks apart. Also, several HRV
variables in the temporal and frequency domains
were analyzed considering other methodological
recommendations such as the menstrual cycle of
females and breathing control for the treatment of
the data. In this sense, there was a loss of
information throughout the entire process, but it
was compensated for by greater methodological
rigor. At the same time, the follow-up throughout
the process ensured that information associated
with other load factors of training (e.g., monotony
and training strain) could be obtained. Future
research may choose to focus on combining weekly
lnRMSSD tracking and the monotony and strain
values derived from the RPEsession. Yet another
limitation of this study was the lack of a control
group to confirm the results and thus increase the
quality of the research. However, this was a high-
standard sample, and it is very difficult to gain
access to the athletes and have them participating
in time commitments beyond their normal training
routine. In the end, this study opted for an
observational design under which all subjects
underwent the same measurements and the same
training process.
In conclusion, the combined analysis of
physiological and psychological factors related to
training of a soccer team contributes much more
valuable information than if these analyses are
performed separately.
Monitoring HRV does not reflect changes
in all physiological systems. Therefore, such
monitoring needs to rely on other factors unrelated
to training such as psychological stress (social
stress, sleep, well-being, etc.).
Monotony is an index derived from the
RPEsession that can be very useful for distributing
sessions in a suitable way and thereby avoiding the
situation in which TLs act as stressors.
The analyses of different HRV domains
can reveal contradictory results in female athletes
because the frequency domain is highly affected by
the menstrual phase and cannot be assessed
during the same week for all members of an all-
female team. To avoid these disturbances, it is
preferable to analyze time-domain variables
because they are more stable and are not altered by
menstruation or by the breathing rate and
108 Physiological and psychological changes at the end of the soccer season in elite female athletes
Journal of Human Kinetics - volume 66/2019
stationary conditions. For this reason, this
measurement is ideal for this type of study of
female athletes. Moreover, the lnRMSSD is the
time-domain variable that exhibited a stronger
correlation with the RESTQ-sport and the Cooper
and Yo-Yo IR1 tests.
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Corresponding author:
Dr. Jose Morales,
c/ Císter, 34, 08022 Barcelona, Spain.
Tel. +34 93 253 30 00 Fax: +34 93 253 30 31
... Study location. The study locations were the United States, 15,33,38 Spain 29,35,37 Brazil, 28,34,36 England, 32,39 and Egypt. 30 Only one did not specify its location. ...
... 32 The study samples ranged from 6 players 32 to 92 players, 31 although most studies did not exceed 20 players. Three studies 15,33,35 examined female soccer players (n = 39) while the other 10 28-32,34,36-39 focused on male soccer players (n = 237). Three studies 29,32,39 examined elite soccer players (n = 24), two studies 35,36 examined professional soccer players (n = 26), three 15,33,38 examined collegiate soccer players (n = 44), and the remaining five examined youth soccer players (n = 192) from U13 to U20. 28,30,31,34,37 Interventions. ...
... Four studies 28,29,34,36 took place during the preseason period and six studies took place during the competitive part of the season. 31,32,35,[37][38][39] The remaining three studies 15,30,33 took place during the off-season period. ...
Full-text available
Heart rate variability (HRV) has become a popular method for monitoring readiness, recovery, and other training-derived parameters to optimize physical performance. This systematic review aimed to examine the impact of training interventions on HRV fluctuations in competitive soccer players. The EBSCO, Web of Sciences (WOS), and Cochrane databases were used for the search. After identifying 293 studies, 13 were included in a qualitative analysis and 6 in a quantitative analysis. Training had a small and nonsignificant effect on HRV in competitive soccer players (Effect Size (ES) = 0.143, Standard Error (SE) = .108, p = .186, 95% confidence interval (CI) [−0.69, 0.355]). Soccer-focused training resulted in higher HRV scores than combined training (Q(2) = 38.13, p = .05). Higher ES were found for frequency domain parameters ( p = .047; d = 0.059), night assessment ( p = .016; d = 0.453), and soccer-focused training ( p = .006; d = 0.366). HRV was also found to have a greater impact on females ( p = .027; d = 0.275) and/or professional soccer players ( p = .007; d = 0.302). Soccer training had a minimal and nonsignificant effect on HRV.
... However, when comparing a training day and a competitive selection day, there were no differences in vmHRV (HFms 2 ) or HRV (LF ms 2 ) (Iellamo et al., 2003). Longitudinally (season of competitive soccer) HRV (HFnu) decreased from pre to post season whereas HRV (LFnu, LF/HF) increased, but mixed results existed regarding relationships between subjective stress ratings and HRV parameters (Morales et al., 2019). When competition stress was controlled for (stress ratings out of competitive season) those with lower perceived stress had higher vmHRV (Mamlouk et al., 2021). ...
... Self-efficacy and self confidence Self-efficacy and shooting performance were compared with vmHRV and it was found self-efficacy and shooting score were positively correlated with vmHRV (RMSSD) (Ortega & Wang, 2018b). Self-confidence was also found to have a positive relationship with vmHRV (RMSSD, pNN50) and HRV (α2) (Morales et al., 2019). Although limited, findings suggest there may be a positive relationship between vmHRV and confidence. ...
... Some of the most common topics utilising HRV across both SEP was to index psychological stress or anxiety. Within the sport psychology domain HRV, stress and anxiety were often linked to competitive responses (Cervantes Blásquez et al., 2009;D'Ascenzi et al., 2014;Iellamo et al., 2003;Morales et al., , 2019Sartor et al., 2017), while exercise psychology mainly focused on the effects of exercise intervention and fitness level on stress and HRV (Boutcher et al., 1998;Franks & Boutcher, 2003;Klaperski et al., 2014;Liu et al., 2015;von Haaren et al., 2016;Zaffalon Junior et al., 2018). Cognitive performance was also explored in both domains with exercise psychology focussing on HRV's influence on cognitive performance in long-term interventions (Albinet et al., 2010;Albinet et al., 2016;Finkenzeller et al., 2011), short -term interventions (Benzing et al., 2016;Ludyga et al., 2019;Murray & Russoniello, 2012) and fitness levels (Alderman & Olson, 2014;Franks & Boutcher, 2003;Luque-Casado et al., 2013;Luque-Casado et al., 2016). ...
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Sport and Exercise Psychology (SEP) often adopts physiological markers in theory and practice, and one measure receiving increasing attention is heart rate variability (HRV). This paper aimed to provide a scoping review of the use of HRV within SEP. The protocol was made available on the Open Science Framework. Study inclusion criteria were examination of HRV in SEP, using athletes or healthy populations, peer-reviewed and published in English. Exclusion criteria were non-peer reviewed work, animal studies, clinical populations, review or conference papers. In February 2022 a systematic search of Web of Science, PubMed and Sport Discus identified 118 studies (4979 participants) using HRV in sport psychology (71) or exercise psychology (47). Risk of bias was assessed via the Mixed Methods Appraisal Tool. A narrative synthesis revealed that HRV was assessed within a range of topics such as stress, overtraining, anxiety, biofeedback, cognitive performance, and sporting performance. Three key limitations within the field were discovered: limited application of theoretical frameworks, methodological issues with HRV measurement, and differing interpretations of HRV results. Future research should use vagally-mediated HRV as a marker of self-regulation and adaptation in SEP, consult relevant HRV theories prior to hypothesis development, and follow methodological guidelines for HRV. ARTICLE HISTORY
... 15 Comprising of 76, 52 and 36 question versions, the RESTQ-Sport has been utilised for individual players as part of a recovery program, 16 and in the training environment. 10,17,18 More recently the RESTQ-Sport has been used to monitor team sport athletes overall stress and recovery during competition in basketball, 19 rugby league, 12,13 rugby union 20,21 and soccer. 22,23 The study in rugby union 20 on adolescent male players in Australia reported that, as the weekly volumes of intensity increased across the competition season, the participants' stress and under recovery also increased. ...
... As a result of this finding, it was recommended 20,21 that the use of the RESTQ-Sport to serially monitor players over a competition period was valuable. In addition to studies on male rugby players, 20,21 the RESTQ-Sport has also been utilised on female soccer 19 and basketball 23 players. To date, no study has reported the stress and recovery of amateur domestic female rugby union players over a season, nor any differences between injured and non-injured players. ...
... Players' performance can be affected by monitoring training load and its physiological effects during preseaseon and seaseon. (Morales et al., 2019). Understanding this issue can help optimize the exercise planning process. ...
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The present study was designed to investigate two aspects. Firstly, analysis of variations in acute load, chronic load, acute-chronic load ratio, monotony, and training strain during the preseason (PS), first and second half of the season (1st HS, 2nd HS). Secondly, comparing these indicators of training load in players' positions. Twenty-two elite soccer players from the Premier League of Iran (age: 27.2 4.5 years, professional experience: 6.2 4.3 years) were involved in this study. Players were monitored daily for 45 weeks through an 18 Hz global positioning system (GPS), to gather data on distance running (total running), distance running at 14 km/h (moderate intensity running), and distance running at a speed above 19.8 km/h (high-intensity running). One-way analysis of variance, followed by Tukey HSD post hoc test to analyze data. The pre-season has a significantly higher amount of acute load, chronic load, monotony and training strain, total running, and moderate-intensity running than the first half ( p < 0.05) and the second half ( p < 0.05). When comparing these training load indicators for high-intensity running, a significant difference was only observed between the PS and the 2nd HS (p 0.05, moderate ES). Also, no significant differences were observed between positions in total running and moderate intensity running. However, training load indices based on high-intensity running between positions showed external defenders vs center backs ( p < 0.05), midfielders ( p < 0.05) and strikers ( p < 0.05) were significantly different. Furthermore, wingers had a significant difference in high-intensity running compared to central defenders ( p < 0.05) and midfielders ( p < 0.05). To conclude, this study demonstrated that acute load, chronic load, monotony, and training strain were more prevalent in the pre-season and slowly decreased during the season. External defenders and wingers experienced more acute load, chronic load, monotony, and training strain for high-intensity running during the season compared to other positions. Therefore, the results indicate that pre-season had a higher physical load than competition season, and players' positions experienced varying physical loads.
... This finding demonstrates the sensitivity of the questionnaire to identify changes in the behavior of the stress-recovery balance of athletes (Kellmann & Kallus, 2016). Several research has been demonstrated the RESTQ-Sport as a reliable method to measure the changes generated by the increase in the training load in team sports such as male volleyball (Berriel et al., 2020;Freitas et al., 2014;Reynoso-Sanchéz et al., 2016) and handball players (Reynoso-Sánchez et al., 2017), female soccer (Morales et al., 2019) and basketball players (Nunes et al., 2014), as well as in individual sports like a study which followed the recovery-stress balance using the RESTQ-Sport in male and female decathletes and pole vaulters . This behavior is reflected in the perception of less recovery in the final measure regarding the initial measure of the RESTQ-Sport. ...
Full-text available
Objectives. – The aim of the study was to analyze the behavior of the modified total quality recovery (TQR) scale during four microcycles of accumulation in volleyball players and its relationship with physiological markers as heart rate variability (HRV) and training impulse (TRIMP), as well as load perception by the session rating of perceived exertion (s-RPE), and perceived recovery-stress state using the RESTQ-Sport questionnaire. Equipment and methods. – Seven female players of a national volleyball team (age: 24.26 ± 3.37 years; weight: 71.63 ± 6.84 kg; height: 176.97 ± 8.04 cm; % fat: 32.64 ± 1.57%) were evaluated during a pre-competitive camp. HRV, TRIMP, TQR scale, and the s-RPE were assessed daily. While the RESTQ-Sport was applied at the beginning and at the end of the concentration camp. The Spearman correlation for all variables, as well as Friedman Test for HRV, TRIMP, TQR and S-RPE and Wilcoxon test for RESTQ-Sport were performed. Furthermore, the smallest worthwhile change (SWC) was applied to identify the magnitudes of change in the intra-subject data. Results. – The TQR scale showed significant correlations (p < .05) with HRV parameters (LnrMSSD and Stress Score), S-RPE and RESTQ-Sport dimensions. Conclusion. – The TQR scale shows to be sensitivity to microcycles of accumulation in female volleyball players. In addition, the study proves the TQR concurrent validity by correlating with different physiological and psychological parameters for monitoring load and recovery. Keywords: assessment; recovery; sports performance; volleyball; internal training load
... HRV has been utilised for a myriad of purposes within traditional sports psychology in both practice and research. This has included indexing psychological stress (Morales et al., 2019), cognitive performance (Gantois et al., 2020), pre-competitive anxiety (Ayuso-Moreno et al., 2020), biofeedback (Pagaduan, 2021), pain (Matylda et al., 2020), motivation (Korobeynikov et al., 2011), and recovery and overtraining (Dobson et al., 2020). Unfortunately, a recent scoping review of this existing literature in traditional sport has revealed a typical lack of theoretical underpinning leading to inconsistent measurement of HRV and consequently highly variable results (Mosley & Laborde, 2022). ...
Full-text available
Heart rate variability (HRV) is a psychophysiological measure of particular interest in esports due to its potential to monitor player self-regulation. This study aimed to systematically review the utilisation of HRV in esports. Consideration was given to the methodological and theoretical underpinnings of previous works to provide recommendations for future research. The protocol was made available on the Open Science Framework. Inclusion criteria were empirical studies, examining HRV in esports, using esports players, published in English. Exclusion criteria were non-peer-reviewed studies, populations with pre-existing clinical illness other than Internet Gaming Disorder (IGD), opinion pieces or review papers. In November 2022 a search of Web of Science, PubMed and EBSCOHost identified seven studies using HRV in esports. Risk of bias was assessed using the Mixed Methods Appraisal Tool. Narrative review identified two primary uses of HRV, investigating stress response and IGD in esports. A lack of theoretical and methodological underpinning was identified as a major limitation of current literature. Further investigation is necessary before making recommendations regarding the use of HRV in esports. Future research should employ sound theoretical underpinning such as the use of vagally mediated HRV and the robust application of supporting methodological guidelines when investigating HRV in esports.
... s) of readiness and the performance measure(s), or only the statistically significant correlations(Berriel et al., 2020;Bok & Jukić, 2019; Cormack et al., 2008;Doeven et al., 2019;Guilhem et al., 2015;Jürimäe et al., 2006;Mangine et al., 2014;Merati et al., 2015;Morales et al., 2019;Purge et al., 2006;Russell et al., 2021;Silva et al., 2014). ...
Full-text available
This systematic review and meta-analysis evaluated the validity of tests / markers of athletic readiness to predict physical performance in elite team and individual sport athletes. Ovid MEDLINE, Embase, Emcare, Scopus and SPORT Discus databases were searched from inception until 15 March 2023. Included articles examined physiological and psychological tests / markers of athletic readiness prior to a physical performance measure. 165 studies were included in the systematic review and 27 studies included in the meta-analysis. 20 markers / tests of athletic readiness were identified, of which five were meta-analysed. Countermovement jump (CMJ) jump height had a large correlation with improved 10m sprint speed / time (r = 0.69; p = .00), but not maximal velocity (r = 0.46; p = .57). Non-significant correlations were observed for peak power (r = 0.13; p = .87) and jump height (r = 0.70; p = .17) from squat jump, and 10m sprint speed / time. CMJ jump height (r = 0.38; p = .41) and salivary cortisol (r = -0.01; p = .99) did not correlate with total distance. Sub-maximal exercise heart rate (r = -0.65; p = .47) and heart rate variability (r = 0.66; p = .31) did not correlate with Yo-Yo Intermittent Recovery Test 1 performance. No correlation was observed between blood C-reactive protein and competition load (r = 0.33; p = .89). CMJ jump height can predict sprint and acceleration qualities in elite athletes. The validity of the other readiness tests / markers meta-analysed warrants further investigation.
... Previous studies have documented the seasonal changes in training load, TM, and TS in professional and collegiate soccer players [9][10][11][12]. Fessi et al. [11] revealed that exposure to higher pre-season training loads resulted in higher TM, TS, and a lower psychophysical state during the in-season. ...
Full-text available
Abstract: The purpose of this study was to analyze the intragroup differences in weekly training monotony (TM) and training strain (TS) between starter and non-starter male professional soccer players at accelerometry based variables throughout the periods of a season. TM and TS of different accelerations and decelerations zones for twenty-one players were followed for forty-eight weeks. Regardless of group, players obtained the highest mean TM (starters = 3.3 ± 0.6, non-starters = 2.2 ± 1.1, in arbitrary unit, AU) and TS (starters = 1288.9 ± 265.2, non-starters = 765.4 ± 547.5, AU) scores in the pre-season for accelerations at Zone 1 (<2 m/s2). The results also indicated that both groups exhibited similar TM and TS scores in accelerations at Zones 2 (2 to 4 m/s2) and 3 (>4 m/s2) across the entire season. While the starters showed the highest TM and TS scores at deceleration Zone 1 (<−2 m/s2) in the end-season, the non-starters exhibited the highest scores at the deceleration Zone 1 in pre-season. It seems that in pre-season, coaches applied higher levels of training with greater emphasis on deceleration for non-starters. This tendency was reduced over time for non-starters, while starters presented higher values of deceleration Zone 1. These results highlight the variations in TM and TS across the different periods of a full season according to match starting status among professional soccer players, and the results suggest that non-starter players should receive higher levels of load to compensate for non-participation in matches throughout a soccer season.
Acute: chronic workload ratio (ACWR) and training monotony have been criticized as injury risk predictors. Therefore, the use of intensity measures should be oriented to understand the variations of intensity across the season. The aim of this systematic review is to summarize the main evidence about the ACWR and training monotony variations over the season in professional soccer players. The search was made in PubMed, SPORTDiscus, and FECYT according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From the 225 studies initially identified, 27 were fully reviewed, and their outcome measures were extracted and analyzed. Existing literature revealed a variety of designs, ACWR and training monotony ranges, variables assessed and durations of the studies. Overall, the range values for ACWR were 0.4–3.39 AU, while those focused on monotony were 0.49–5.7 AU. Regarding ACWR, the ratios located around 0.85-1.25 could predict lower risk values and ratios around ≥ 1.50 could predict higher risk values. On the contrary, with respect to training monotony, the ratios are approximately between 0.5 to 2.00 (low values in the preseason and low competition weeks and high values when soccer players are in highly scheduled competition weeks). Nevertheless, ACWR and training monotony methods should be addressed and considered based on their real value before using this indicator to reduce injury risk. In fact, the data did not conclusively define injured and non-injured players. For this reason, utilizing standardized approaches will allow for more precise conclusions about professional soccer players.
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One of the efforts to prevent the overreaching condition is by doing a recovery phase after exercise. The quality and quantity of recovery influence the effectiveness of recovery. The effectiveness of recovery can be observed by heart rate and body temperature after recovery. This study aims to compare active recovery and passive recovery after moderate-intensity continuous training on heart rate and body temperature. The research method used is quasi-experimental and uses a two-group pre and post-test design. Based on the Pocock formula, the research subjects used were 40 women aged 24 years to 35 years. The data collected include heart rate and body temperature. The data analysis techniques used were normality test, treatment effect test (paired sample t-test), and difference test (independent samples t-test). The results showed that active recovery was better than passive recovery to optimizing post-exercise heart rate (p<0.05). Active recovery was better than passive recovery in optimizing body temperature (p<0.05). It was concluded that active recovery after moderate-intensity continuous training was better than passive recovery to optimize post-exercise recovery and prevent overreaching.
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The purpose of this study was to track changes in training load (TL) and recovery status indicators throughout a 2-week preseason and to interpret the meaning of these changes on an individual basis among 8 Division-1 female soccer players. Weekly averages for heart rate variability (lnRMSSD), TL and psychometrics were compared with effect sizes (ES) and magnitude based inferences. Relationships were determined with Pearson correlations. Group analysis showed a very likely moderate decrease for total training load (TTL) (TTL week 1 = 1203 ± 198, TTL week 2 = 977 ± 288; proportion = 1/2/97, ES = -0.93) and a likely small increase in lnRMSSD (week 1 = 74.2 ± 11.1, week 2 = 78.1 ± 10.5; proportion = 81/14/5, ES = 0.35). Fatigue demonstrated a very likely small improvement (week 1 = 5.03 ± 1.09, week 2 = 5.51 ± 1.00; proportion = 95/4/1; ES = 0.45) while the other psychometrics did not substantially change. A very large correlation was found between changes in TL and lnRMSSD (r = -0.85) while large correlations were found between lnRMSSD and perceived fatigue (r = 0.56) and soreness (r = 0.54). Individual analysis suggests that 2 subjects may benefit from decreased TL, 2 subjects may benefit from increased TL and 4 subjects may require no intervention based on their psychometric and lnRMSSD responses to the TL. Individual weekly changes in lnRMSSD varied among subjects and related strongly with individual changes in TL. Training intervention based on lnRMSSD and wellness responses may be useful for preventing the accumulation of fatigue in female soccer players.
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This study investigated the concurrent validity of the Spanish version of the Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) by comparing it with the Profile of Mood States (POMS) and State-Anxiety scale of the State-Trait Anxiety Inventory (STAI). A total of 194 athletes (53% males and 47% females) participated in the study. The POMS scales Tension, Depression, Anger, Fatigue and Confusion correlated positively with the stress scales and factors and negatively with the RESTQ-Sport's recovery scales and factors, while the POMS Vigour scale correlated positively with the RESTQ-Sport's recovery scales and factors and negatively with the stress scales and factors. State-anxiety correlated positively with stress and negatively with the recovery scales and factors. The data obtained confirm the Spanish version of the RESTQ-Sport's concurrent validity.
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Purpose To assess the validity of RR intervals and short- term heart rate variability (HRV) data obtained from the Polar V800 heart rate monitor, in comparison to an electro- cardiograph (ECG). Method Twenty participants completed an active orthos- tatic test using the V800 and ECG. An improved method for the identi cation and correction of RR intervals was employed prior to HRV analysis. Agreement of the data was assessed using intra-class correlation coef cients (ICC), Bland–Altman limits of agreement (LoA), and effect size (ES). Results A small number of errors were detected between ECG and Polar RR signal, with a combined error rate of 0.086 %. The RR intervals from ECG to V800 were sig- ni cantly different, but with small ES for both supine cor- rected and standing corrected data (ES <0.001). The bias (LoA) were 0.06 (−4.33 to 4.45 ms) and 0.59 (−1.70 to 2.87 ms) for supine and standing intervals, respectively. The ICC was >0.999 for both supine and standing cor- rected intervals. When analysed with the same HRV soft- ware no signi cant differences were observed in any HRV parameters, for either supine or standing; the data displayed small bias and tight LoA, strong ICC (>0.99) and small ES (≤0.029).
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Background: Heart Rate Variability (HRV), which is a measure of the cardiac autonomic tone, displays physiological changes throughout the menstrual cycle. The functions of the ANS in various phases of the menstrual cycle were examined in some studies. Aims and objectives: The aim of our study was to observe the effect of menstrual cycle on cardiac autonomic function parameters in healthy females. Materials and methods: A cross-sectional (observational) study was conducted on 50 healthy females, in the age group of 18-25 years. Heart Rate Variability (HRV) was recorded by Physio Pac (PC-2004). The data consisted of Time Domain Analysis and Frequency Domain Analysis in menstrual, proliferative and secretory phase of menstrual cycle. Data collected was analysed statistically using student's pair t-test. Results: The difference in mean heart rate, LF power%, LFnu and HFnu in menstrual and proliferative phase was found to be statistically significant. The difference in mean RR, Mean HR, RMSSD (the square root of the mean of the squares of the successive differences between adjacent NNs.), NN50 (the number of pairs of successive NNs that differ by more than 50 ms), pNN50 (the proportion of NN50 divided by total number of NNs.), VLF (very low frequency) power, LF (low frequency) power, LF power%, HF power %, LF/HF ratio, LFnu and HFnu was found to be statistically significant in proliferative and secretory phase. The difference in Mean RR, Mean HR, LFnu and HFnu was found to be statistically significant in secretory and menstrual phases. Conclusion: From the study it can be concluded that sympathetic nervous activity in secretory phase is greater than in the proliferative phase, whereas parasympathetic nervous activity is predominant in proliferative phase.
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Professional football is a contact sport with a high risk of injury. This study was designed to examine the contribution of stress and recovery variables as assessed with the Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) to the risk of injury in professional football players. In a prospective, non-experimental cohort design, 22 professional football players in the highest German football league were observed over the course of 16 months. From January 2010 until April 2011, the players completed the RESTQ-Sport a total of 222 times in monthly intervals. In addition, injury data were assessed by the medical staff of the club. Overall, 34 traumatic injuries and 10 overuse injuries occurred. Most of the injuries were located in the lower limb (79.5%), and muscle and tendon injuries (43.2%) were the most frequently occurring injury type. In a generalised linear model, the stress-related scales Fatigue (OR 1.70, P = 0.007), Disturbed Breaks (OR 1.84, P = 0.047) and Injury (OR 1.77, P < 0.001) and the recovery-related scale Sleep Quality (OR 0.53, P = 0.010) significantly predicted injuries in the month after the assessment. These results support the importance of frequent monitoring of recovery and stress parameters to lower the risk of injuries in professional football.
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This study aimed to examine the alterations in physiological and biochemical markers, after 17 weeks of ballet training in high level ballet dancers. Twenty four female ballet dancers from 12 to 15 years old took part in the study. The study followed 17 weeks of ballet training and analyzed changes in body composition, the autonomic nervous system and biochemical variables before and after (post) training. The internal training load was obtained using the session rating of perceived exertion (session-RPE) method, calculated as the mean weekly session-RPE, monotony and strain. After 17 weeks of training there were significant increases in body mass, height, lean body mass, total protein, urea, hemoglobin concentration, testosterone and thyroxine. During this period, decreases in relative body fat, uric acid, red blood cells, C-reactive protein, and ferritin were also found. After the training period, the autonomic modulation demonstrated significant positive alterations, such as increases in parasympathetic related indices. Based on the results obtained we concluded that ballet training led to improvements in body composition and autonomic modulation. In general hematological and biochemical variables demonstrated that the training did not have adverse effects on the health state of the adolescents
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This study evaluated the 7-day mean and CV of supine and standing ultra-short log transformed root mean square of successive R-R intervals multiplied by 20 (lnRMSSDx20) obtained with a smartphone application (app) in response to varying weekly training load (TL). Additionally, we aimed to determine if these values could be accurately assessed in as few as 5 or 3 days per week. 9 females from a collegiate soccer team performed daily HRV measures with an app in supine and standing positions over 3 weeks of moderate, high and low TL. The mean and CV over 7, 5, and 3 days were compared within and between each week. The 5 and 3-day measures within each week provided very good to near perfect intraclass correlations (ICCs ranging from 0.74 - 0.99) with typical errors ranging from 0.64 - 5.65 when compared with the 7-day criteria. The 7, 5, and 3-day supine CV and the 7-day standing CV were moderately lower during the low load compared to the high load week (p values ranged from 0.003 - 0.045 and effect sizes ranged from 0.86 - 0.92), with no significant changes occurring in the other measures. This study supports the use of the mean and CV of lnRMSSD measured across at least 5 days for reflecting weekly values. The supine lnRMSSDx20 CV as measured across 7, 5, and 3 days was the most sensitive marker to the changes in TL within the 3-week period.
Since publication of its First Edition in 1981, Exercise Physiology has helped more than 350,000 students build a solid foundation of the scientific principles underlying modern exercise physiology. This Seventh Edition has been thoroughly updated with all the most recent findings, guiding you to the latest understanding of nutrition, energy transfer, and exercise training and their relationship to human performance. This Seventh Edition maintains its popular seven-section structure. It begins with an exploration of the origins of exercise physiology and concludes with an examination of the most recent efforts to apply principles of molecular biology. The book provides excellent coverage of exercise physiology, uniting the topics of energy expenditure and capacity, molecular biology, physical conditioning, sports nutrition, body composition, weight control, and more. Every chapter has been fully revised and updated to reflect the latest information in the field. The updated full-color art program adds visual appeal and improves understanding of key topics. A companion website includes over 30 animations of key exercise physiology concepts; the full text online; a quiz bank; references; appendices; information about microscope technologies; a timeline of notable events in genetics; a list of Nobel Prizes in research related to cell and molecular biology; the scientific contributions of thirteen outstanding female scientists; an image bank; a Brownstone test generator; PowerPoint® lecture outlines; and image-only PowerPoint® slides.
The RESTQ was developed to measure the frequency of current stress symptoms along with the frequency of recovery-associated activities. Through the simultaneous assessment of stress and recovery, a differentiated picture of the current recovery-stress state can be provided. Five forms of the RESTQ are available. A general version (RESTQ-Basic) with seven stress scales and five recovery scales is the foundation for the specific versions for athletes (RESTQ-Sport), for coaches (RESTQ-Coach), for adolescents (RESTQ-CA) and for the work context (RESTQ-Work). All versions contain scales measuring specific aspects of stress and recovery in their field. The modular structure is the unique feature of the RESTQ. Each version has its specific time frame of three or seven days/nights. A Likert-type scale is used with values ranging from 0 (never) to 6 (always) indicating how often the respondent participated in various activities or experienced relevant states. The profile of the RESTQ scales provides valuable information immediately on areas where improvement is needed. The questionnaire is useful for research on stress and recovery and ideal for applied settings. While the manual is provided in English, English and German speaking samples have been used to provide data of psychometrics. The German questionnaires are available as print version; the English versions are available on enquiry.
The two Yo-Yo intermittent recovery (IR) tests evaluate an individual’s ability to repeatedly perform intense exercise. The Yo-Yo IR level 1 (Yo-Yo IR1) test focuses on the capacity to carry out intermittent exercise leading to a maximal activation of the aerobic system, whereas Yo-Yo IR level 2 (Yo-Yo IR2) determines an individual’s ability to recover from repeated exercise with a high contribution from the anaerobic system. Evaluations of elite athletes in various sports involving intermittent exercise showed that the higher the level of competition the better an athlete performs in the Yo-Yo IR tests. Performance in the Yo- Yo IR tests for young athletes increases with rising age. The Yo-Yo IR tests have shown to be a more sensitive measure of changes in performance than maximum oxygen uptake. The Yo-Yo IR tests provide a simple and valid way to obtain important information of an individual’s capacity to perform repeated intense exercise and to examine changes in performance.