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The aim of this study was to assess the reliability of a novel field test of critical running speed (CS). Ten trained male distance runners completed a familiarisation trial plus three separate experimental trials on a standard 400 m athletics track. Each trial consisted of three distances (1200, 2400 and 3600 metres) that were selected to produce finishing times in the region of 3, 7 and 12 minutes respectively. Participants were instructed to cover the set distance in the fastest time possible. Participants rested for 30 minutes between efforts. Data were modelled using the linear distance-time model, described by the equation: d = (CS x t) + ARC, where: d = distance run (m), t = running time (s), and ARC = anaerobic running capacity (m). Results demonstrated a coefficient of variation (CV) of 2.0% (95% confidence limit (95% CL): 1.4–3.8%) for trials 2–1 and 1.3% (95% CL: 0.9–2.4%) for trials 3–2. There was no significant difference in CS (m·s-1) across trials (P<0.05). The limits of agreement were ±0.27m·s-1 of the measure for trials 2–1 and ±0.18 m·s-1 for trials 3–2. ARC proved to be less reliable with a group CV of 18.4% (95% CL: 13.5–39.9%) for trials 2–1 and 9.8% (95% CL: 7.0–19.6%) for trials 3–2. Although the assessment of ARC is less reliable, CV data are similar to those reported previously during laboratory-based testing.
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Volume 1 • Issue 1 • 1000101
J Sport Medic Doping Studie
ISSN: 2161-0673 JSMDS, an open access journal
Sports Medicine & Doping Studies
Galbraith et al. J Sport Medic Doping Studie 2011, 1:1
http://dx.doi.org/10.4172/2161-0673.1000101
Research Article Open Access
A Novel Field Test to Determine Critical Speed
Galbraith A, Hopker JG*, Jobson SA and Passeld L
Centre for Sports Studies, University of Kent, Kent, England
Abstract
The aim of this study was to assess the reliability of a novel eld test of critical running speed (CS). Ten trained
male distance runners completed a familiarisation trial plus three separate experimental trials on a standard 400 m
athletics track. Each trial consisted of three distances (1200, 2400 and 3600 metres) that were selected to produce
nishing times in the region of 3, 7 and 12 minutes respectively. Participants were instructed to cover the set distance
in the fastest time possible. Participants rested for 30 minutes between efforts. Data were modelled using the linear
distance-time model, described by the equation: d = (CS x t) + ARC, where: d = distance run (m), t = running time
(s), and ARC = anaerobic running capacity (m). Results demonstrated a coefcient of variation (CV) of 2.0% (95%
condence limit (95% CL): 1.4–3.8%) for trials 2–1 and 1.3% (95% CL: 0.9–2.4%) for trials 3–2. There was no
signicant difference in CS (m·s
-1
) across trials (P<0.05). The limits of agreement were ±0.27m·s
-1
of the measure
for trials 2–1 and ±0.18 m·s
-1
for trials 3–2. ARC proved to be less reliable with a group CV of 18.4% (95% CL: 13.5–
39.9%) for trials 2–1 and 9.8% (95% CL: 7.0–19.6%) for trials 3–2. Although the assessment of ARC is less reliable,
CV data are similar to those reported previously during laboratory-based testing.
*Corresponding author: James G. Hopker, Centre for Sports Studies, University
of Kent, Kent, ME4 4AG, England, Tel: +44 1634 88 88 14; Fax: +44 1634 88 88
90; E-mail: jgh@kent.ac.uk
Received September 04, 2011; Accepted October 28, 2011; Published October
31, 2011
Citation: Galbraith A, Hopker JG, Jobson SA, Passeld L (2011) A Novel Field Test
to Determine Critical Speed. J Sport Medic Doping Studie 1:101. doi:10.4172/2161-
0673.1000101
Copyright: © 2011 Galbraith A, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
Keywords: Critical velocity; Endurance; Running, Economy;
Anaerobic work capacity
Introduction
It has been suggested that the Critical Power (CP) demarcates the
heavy and severe exercise domains [1] and as such corresponds to an
exercise intensity which lies between that associated with the lactate
threshold and that eliciting
2max
VO
[2]. Consequently, CP has been
associated with overall athletic performance in long-duration events
[3,4]. e concept of CP has been applied to treadmill running [5],
that is, where the relation between treadmill running velocity and time
to exhaustion conforms to a hyperbolic function similar to that seen
in cycling. is relationship has traditionally been termed Critical
Velocity, however as the present study utilised a eld test where
subjects were required to run a set number of laps of an athletics track,
Critical Speed (CS) is a more appropriate term. erefore, to allow
standardisation of terminology CS will be used for the remainder of
the paper, regardless of whether the reference is to treadmill or eld
testing.
Many early ‘critical power’ studies calculated CP by plotting the
total work done against the time taken to complete that work. For
running exercise this model has been transformed into a distance-
time model, where the total distance covered was plotted against the
time taken to cover that distance [6]. is transformation of the CP
model can be described by a linear relationship, where the slope of the
regression line calculates CS and the y-intercept is termed anaerobic
running capacity (ARC).
e traditional method of testing CS in a laboratory involves
athletes completing a set number of time-to-exhaustion (TTE) trials at a
constant speed on the treadmill. Constant speed trials have been shown
to have poor reliability with coecients of variation ranging from 15.1%
to 25% [7,8]. is is supported by similar research in both cycling and
swimming, which also demonstrated the poor reliability of constant
power/speed trials [9,10]. However, research into the reliability of the
CS and ARC parameters is limited. Hinckson and Hopkins (2005)
looked at the reliability of CS and ARC measured on a treadmill. ey
demonstrated good reliability of CS data (coecient of variation 1.8%),
but poor reliability of ARC data (coecient of variation 14%). ese
researchers used constant speed trials where participants were required
to run to exhaustion at three pre-set constant speeds that resulted in
exhaustion times of approximately 1–2, 3–4 and 7–10 minutes.
Constant distance trials, where the athlete is required to cover a
set distance in the fastest possible time, have been shown to have a
far better reliability, with coecients of variation ranging from 3.3%
to 3.7% [7,11]. Due to the limitations of the manual speed control
measures on standard motorised treadmills, such trials are arguably
best performed in a eld-based setting. However, there appears to be
no research on the reliability of CS and ARC using constant distance
trials. erefore, the purpose of the current study was to assess the
reliability of CS and ARC determined in the eld on an athletics track.
Materials and Methods
Subjects
Following institutional ethics approval, ten trained male middle
distance runners (age: 22±4yrs;
2max
VO
69.1±4.2mL.kg
-1
.min
-1
) were
recruited for the study. All athletes were competitive club or national
standard runners who had been competing for a minimum of 2 years.
Subjects refrained from heavy exercise in the 24 hours prior to all tests
and from food in the 3 hours prior to all tests. Tests for individual
subjects were completed at the same time of day to eliminate a possible
eect of circadian rhythms [12].
Experimental design
Each subject completed ve experimental visits. At visit 1, subjects
completed an incremental exercise test to determine
2max
VO
,
whilst
during visit 2 subjects completed a familiarisation of the eld test
protocol. During visits 3, 4 and 5 subjects completed repeated tests of
the eld test protocol.
Volume 1 • Issue 1 • 1000101
J Sport Medic Doping Studie
ISSN: 2161-0673 JSMDS, an open access journal
Citation: Galbraith A, Hopker JG, Jobson SA, Passeld L (2011) A Novel Field Test to Determine Critical Speed. J Sport Medic Doping Studie 1:101.
doi:10.4172/2161-0673.1000101
Page 2 of 4
Determination of subject characteristics
Subjects completed a 5-min self-paced warm-up [13], on an
H/P/Cosmos Saturn 4.0 treadmill (H/P/Cosmos Sports and Medical,
Nussdorf-Traunstein, Germany) set to a 1% gradient as recommended
by Jones and Doust (1996). Following a 5-min self-selected stretching
routine, subjects completed a two-phase protocol [14] to determine
running economy (mL.kg
-1
.km
-1
),
2max
VO
, and velocity at
2max
VO
(v
2max
VO
). At the end of each 3-min stage during phase 1, treadmill
speed was increased by 1.0 km·h
-1
. Phase 1 of the protocol was
terminated when the subject reached a lactate concentration >4.0
mmol·L
-1
. Following a 15-minute recovery, the second phase of the
test was initiated at a speed 2.0 km·h
-1
below the speed at which the
subject completed phase 1. Whilst treadmill speed remained constant
throughout phase 2 of the protocol, treadmill gradient was increased by
1% every minute until volitional exhaustion. Pulmonary gas exchange
was measured breath-by-breath (MetaLyser 3B, Cortex Biophysik,
Leipzig, Germany).
2max
VO
was recorded as the highest mean oxygen
consumption over a 60-s period.
Determination of critical speed
Critical Speed was calculated from three constant distance runs
(3600, 2400, 1200 m) carried out on a competition standard 400 m
outdoor running track. ese distances were estimated to yield n-
ishing times between 2 and 12 min [5]. Testing was only carried out
when wind speed was less than 2.0 m·s
-1
[15]. Subjects completed a
standardised warm-up (5-min self-paced jogging, followed by a 5-min
stretching routine). Subjects were then instructed to cover the set dis-
tance in the fastest time possible. Finishing times for the three distances
were recorded to the nearest second. All three runs were conducted in
the order of longest to shortest, on the same day, with a 30-min rest be-
tween them. Linear regression was used to calculate CS and ARC from
the results of these trials using the d = (CS x t)+ARC model, where: d =
distance run (m), CS = critical running speed (m·s
-1
), t = running time
(s), and ARC = anaerobic running capacity (m).
Data analysis
Data were assessed for normality of distribution. To assess the sta-
bility reliability of CS and ARC, the within-subject variation, expressed
as a coecient of variation (CV), was derived from log-transformed
data [16]. e 95% condence intervals were calculated for each CV.
Condence intervals (95% CI) of the CV and 95% limits of agreement
were calculated per participant to assess the variability of the repeated
tests [16]. Comparisons of CS and ARC across days were assessed us-
ing repeated measures ANOVA. Statistical signicance was set at 95%
condence (P < 0.05). Results are reported as mean ± SD unless oth-
erwise stated.
Results
A mean group typical error, expressed as a coecient of variation,
of 2.0% (95% condence limit (95% CL): 1.4–3.8%) for trials 2–1
and 1.3% (95% CL: 0.9–2.4%) for trials 3–2 was found. ere was no
signicant dierence in CS across trials (P<0.05). Repeated measures
ANOVA also conrmed the absence of an order eect in the data. e
limits of agreement were ±0.27m·s
-1
of the measure for trials 2–1 and
±0.18 m·s
-1
for trials 3–2, (Figure 1). ARC proved to be less reliable with
a group CV of 18.4% (95% CL: 13.5–39.9%) for trials 2–1 and 9.8%
(95% CL: 7.0–19.6%) for trials 3–2, (Figure 2), although this variability
did not result in signicant dierences between trials (P>0.05).
Based on a mean CS of 4.72m.s
-1
and the mean CV for CS of 1.7%,
an athlete would have to improve their CS by 0.08m.s
-1
in order to
detect a meaningful change in performance. eoretically this could
be achieved by an improvement of just over 1 second per lap during the
constant distance trials.
Discussion
e results of the current study demonstrate that critical speed
can be reliably tested using a novel same day eld test. e mean CV
of 1.7% is similar to that previously reported in the literature [17] for
laboratory based testing of CS. A 5% coecient of variation has been
cited as an acceptable upper limit in sports science reliability studies
[18]. Given that the CV values observed were below this boundary we
might consider CS from the novel eld test to be reliable. In agreement
with previous literature [17] ARC proved to be less reliable, with a CV
of 14.1%. erefore, the CS and ARC reliability results of the current
study are similar to those reported previously during laboratory-
based testing. However, such a level of variation in ARC is unlikely
to be acceptable when evaluating the relatively small training-induced
changes seen in well-trained athletes [16]. Such a conclusion is
supported by limits of agreement analyses which suggest that, with an
approximate 95% probability, the dierences between the test and retest
of ARC in an individual from the well-trained running population will
at best, lie between ± 48m.
Assuming that the bias is negligible, ratio limits of agreement
suggest that, between any two tests, CS may typically dier by 4.7%
and ARC by 39.0%, in a positive or negative direction. e coecient
of variation for both CS and ARC decreased from trials 2–1 to trials
3–2, although there were no signicant dierences in CS or ARC across
trials (P<0.05). ese results suggest the need for several familiarisation
trials before using the novel constant distance eld trial to monitor
performance.
Most of the previous literature investigating CS has required a
subject to run at a set speed until exhaustion. ese have traditionally
been shown to have poor reliability with coecients of variation
ranging from 15.1 to 25% [7,8] Similar ndings have been reported
in both cycling [9] and swimming [10]. Hinckson and Hopkins [17]
used a variety of approaches to produce estimates of test-retest error
of measurement calculated from times to exhaustion. All reliability
estimates were <3%, and some were ~1%, resulting in the authors
stating that their ndings should lay to rest any concerns that time
to exhaustion is inherently an unreliable measure of endurance
performance.
A major criticism can be levelled at the use of constant speed trials
in testing CS, typically performed on a treadmill. ese trials are not
ecologically valid, and do not mimic any training or race situation for
a competitive athlete. In training and racing athletes are required to
cover a set distance in the fastest time possible, and are rarely (if ever),
required to run at a constant speed until exhaustion. In the current
study we decided to take the more ecologically valid approach of using
constant distance trials. Even so, one disadvantage of this approach is
the potential inuence of pacing. e potential impact of poor pacing
strategy was decreased in the current study by the selection of trained
distance runners as participants. However, alterations in pacing might
indicate why the CV decreased over the time course of the repeated
experimental trials.
A novel aspect of the constant distance eld trial used in the
present study was that each of the individual runs used to model CS
and ARC were completed with a 30-min recovery period between
them. is allowed the whole testing session to be completed within
Volume 1 • Issue 1 • 1000101
J Sport Medic Doping Studie
ISSN: 2161-0673 JSMDS, an open access journal
Citation: Galbraith A, Hopker JG, Jobson SA, Passeld L (2011) A Novel Field Test to Determine Critical Speed. J Sport Medic Doping Studie 1:101.
doi:10.4172/2161-0673.1000101
Page 3 of 4
a 2-hour time frame. Traditionally when CS and ARC are tested in a
laboratory on a treadmill, recovery periods in excess of 24 hours are
commonly used [13,19] making this a protracted approach. e results
of the current study demonstrate that the constant distance eld trial
is a reliable method of assessing CS and ARC that may present a more
attractive option to sports scientists, athletes and coaches wishing to
monitor physical tness, endurance performance, and design optimal
pacing strategies.
Using the novel eld test a coach could gain information on an
athlete’s aerobic and anaerobic capabilities, from the CS and ARC
parameters respectably. As the testing procedure takes a relatively
short length of time, these parameters could be monitored at regular
intervals through a season to assess the impact of training on CS and
ARC. Previous research in cycling has indicated that Critical Power
increases following a period of interval endurance training [20,21],
and Anaerobic Work Capacity increases following a period of power
or sprint training [22].
Using the following equation the distance-time relationship can be
used to calculate the quickest time in which an athlete could complete
a set distance:
t = (D-ARC)/CS [23]
Where t = predicted time taken to complete a set distance and D = the
chosen set distance.
is prediction of performance could provide a runner with a
realistic target to aim for in competitive races. Predicted performance
from the distance-time relationship has shown good correlation with
actual performance over distances ranging from 10,000m [24] to the
Marathon (Florence and Weir, 1997).
An athlete and their coach could also use information obtained
from the distance-time relationship to formulate pacing and tactical
strategies aimed at maximizing competitive performance [25]. For
example, in a competitive race situation the best tactical pacing strategy
for an athlete with a relatively low CS but a high ARC, might be to slow
the pace and use their high ARC to full eect in a sprint nish [25].
Finally, it has been suggested that the distance-time relationship
can be used to rank runners in terms of ability. Successful athletes not
only need a high
2max
VO
, but also the ability to sustain a high percentage
of this value for the duration of their event - i.e. they need good aerobic
endurance. Gamelin et al. (2006) suggest that CS takes into account
both
2max
VO
and aerobic endurance. erefore, they suggest that CS
should be used to rank middle and long distance runners with regard
to their ability in long-distance running events.
Conclusion
e results of the current study demonstrate that a novel constant
distance eld trial reliably assesses CS and produces reliability data
comparable to that previously reported using constant speed trials. Al-
Figure 1: Bland-Altman plots of the Critical Running Speed test–re-test differences between trials 1 and 2 [left] and trials 2 and 3 [right]. The solid horizontal lines
represent mean bias, whilst the dashed lines represent the 95% limits of agreement.
Figure 2: Bland-Altman plots of the Anaerobic Running Capacity test–re-test differences between trials 1 and 2 [left] and trials 2 and 3 [right]. The solid horizontal
lines represent mean bias, whilst the dashed lines represent the 95% limits of agreement.
Volume 1 • Issue 1 • 1000101
J Sport Medic Doping Studie
ISSN: 2161-0673 JSMDS, an open access journal
Citation: Galbraith A, Hopker JG, Jobson SA, Passeld L (2011) A Novel Field Test to Determine Critical Speed. J Sport Medic Doping Studie 1:101.
doi:10.4172/2161-0673.1000101
Page 4 of 4
though the assessment of ARC is less reliable, coecients of variation
are also similar to those reported previously during laboratory-based
testing. erefore, the novel constant distance eld trial could be used
as a suitable, more ecologically valid alternative to treadmill based con-
stant speed trials when assessing CS and ARC.
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... Each participant performed three time trials (TTs) on the track field (1: 2,600 m; 2: 1,800 m; and 3: 1,000 m). These TTs were selected according to Galbraith et al. (2011) and Hughson et al. (1984) to result in completion times between 3 and 12 min before volitional exhaustion. Consistent with Triska et al. (2017) and Galbraith et al. (2011), the sequence of TTs was conducted in the order of the longest to the shortest effort, on the same day, with a 30-min rest period to ensure a fully reconstituted D (maximum distance covered above the CS). ...
... These TTs were selected according to Galbraith et al. (2011) and Hughson et al. (1984) to result in completion times between 3 and 12 min before volitional exhaustion. Consistent with Triska et al. (2017) and Galbraith et al. (2011), the sequence of TTs was conducted in the order of the longest to the shortest effort, on the same day, with a 30-min rest period to ensure a fully reconstituted D (maximum distance covered above the CS). The participants completed a 5-min self-paced low-intensity warm-up exercise and were encouraged to cover the set TTs as quickly as possible; time was measured using a stopwatch (Galbraith et al., 2014). ...
... The participants completed a 5-min self-paced low-intensity warm-up exercise and were encouraged to cover the set TTs as quickly as possible; time was measured using a stopwatch (Galbraith et al., 2014). The CS was estimated through a linear regression between the distance run (d) and t lim using the d = (CS × t lim ) + D model, where d is the distance run (in meters), CS the critical speed (in meters per second), t lim the time to exhaustion (in seconds), and D is the maximum distance covered (in meters) above the CS (Hughson et al., 1984;Galbraith et al., 2011). CS was estimated through the combinations of three (CS 1 , 2 , 3 ) and two TTs (CS 1 , 2 , CS 1 , 3 , and CS 2 , 3 ). ...
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This study aimed to examine which variable, between the peak running velocity determined on the track field (Vpeak_TF) and critical speed (CS), is the best predictor of the 5-km running performance in recreational runners. Twenty-five males performed three tests to determine the Vpeak_TF, CS, and 5-km running performance on the track field, with a minimal interval of 48 h between each test. The Vpeak_TF protocol started with a velocity of 8 km⋅h–1, followed by an increase of 1 km⋅h–1 every 3 min until volitional exhaustion, which was controlled by sound signals, with cones at every 25 m indicating when the participants were required to pass the cone’s position to maintain the required velocity. The participants performed three time trials (TTs) (1: 2,600 m; 2: 1,800 m; and 3: 1,000 m) on the same day, with a 30-min rest period to determine the CS through the combinations of three (CS1,2,3) and two TTs (CS1,2, CS1,3, and CS2,3). The 5-km running performance time was recorded to determine the test duration, and the mean velocity (MV) was calculated. There was a significant difference observed between the Vpeak_TF and the MV 5-km running performance. However, no differences were found between the CS values and the MV 5-km running performance. A correlation was observed between the Vpeak_TF (R = −0.90), CS1,2,3 (R = −0.95), CS1,3 (R = −0.95), and the 5-km running performance time. Linear regression indicated that the Vpeak_TF (R2 = 0.82), CS1,2,3 (R2 = 0.90), and CS1,3 (R2 = 0.90) significantly predicted the 5-km running performance time. The CS results showed a higher predictive power for the 5-km running performance, slightly better than the Vpeak_TF. Also, CS1,2,3 and the CS1,3 presented the highest predictive power for the 5-km running performance of recreational runners.
... The linear distance-time model is represented by d 5 (CSÅ; t) 1 D9, where d 5 distance run and t 5 running time. This protocol is the adapted from described by Galbraith et al. (12). ...
... Significant increases in peak lactate (F (2,12) Table 4. ...
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Jones, TW, Shillabeer, BC, Ryu, JH, and Cardinale, M. Development in adolescent middle-distance athletes: a study of training loadings, physical qualities, and competition performance. J Strength Cond Res XX(X): 000-000, 2019-The purpose of this study was to examine changes in running performance and physical qualities related to middle-distance performance over a training season. The study also examined relationships between training loading and changes in physical qualities as assessed by laboratory and field measures. Relationships between laboratory and field measures were also analyzed. This was a 9-month observational study of 10 highly trained adolescent middle-distance athletes. Training intensity distribution was similar over the observational period, whereas accumulated and mean distance and training time and accumulated load varied monthly. Statistically significant (p < 0.05) and large effect sizes (Cohen's d) (>0.80) were observed for improvements in: body mass (5.6%), 600-m (4.6%), 1,200-m (8.7%), and 1,800-m (6.1%) time trial performance, critical speed (7.1%), V[Combining Dot Above]O2max (5.5%), running economy (10.1%), vertical stiffness (2.6%), reactive index (3.8%), and countermovement jump power output relative to body mass (7.9%). Improvements in 1,800 m TT performance were correlated with increases in V[Combining Dot Above]O2max (r = 0.810, p = 0.015) and critical speed (r = 0.918, p = 0.001). Increases in V[Combining Dot Above]O2max and critical speed were also correlated (r = 0.895, p = 0.003). Data presented here indicate that improvements in critical speed may be reflective of changes in aerobic capacity in adolescent middle-distance athletes.
... The third protocol was used in the studies on the modelling of running performances that were based on the world records [17][18][19][20] or performances in the Olympic games [21] or individual performance of elite endurance runners [15]. The reliability of performances in protocol 1 (constant speed) is low, whereas the reliability of the other protocols is higher [22][23][24][25][26][27]. For example in swimming, the Coefficient of Variation of constant-speed protocol (CV = 6.46 ± 6.24%) was significantly less reliable (p < 0.001) than those of constant-time protocol (CV = 0.63 ± 0.54%) and constant-distance protocol (CV = 0.56 ± 0.60%) [27]. ...
... Previous experimental studies [22][23][24][25][26][27] showed that performance reliability with constant-speed protocol is significantly lower than those with the other protocols (constant-time or constant-distance protocols). However, for SCrit or PCrit in the present theoretical study, the effects of 20%-submaximal performances in protocol 1 are lower than the effects of 5%-submaximal performances in protocols 2 and 3. ...
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The effects of submaximal performances on critical speed (SCrit) and critical power (PCrit) were studied in 3 protocols: a constant-speed protocol (protocol 1), a constant-time protocol (protocol 2) and a constant-distance protocol (protocol 3). The effects of submaximal performances on SCrit and PCrit were studied with the results of two theoretical maximal exercises multiplied by coefficients lower or equal to 1 (from 0.8 to 1 for protocol 1; from 0.95 to 1 for protocols 2 and 3): coefficient C1 for the shortest exercises and C2 for the longest exercises. Arbitrary units were used for exhaustion times (tlim), speeds (or power-output in cycling) and distances (or work in cycling). The submaximal-performance effects on SCrit and PCrit were computed from two ranges of tlim (1–4 and 1–7). These effects have been compared for a low-endurance athlete (exponent = 0.8 in the power-law model of Kennelly) and a high-endurance athlete (exponent = 0.95). Unexpectedly, the effects of submaximal performances on SCrit and PCrit are lower in protocol 1. For the 3 protocols, the effects of submaximal performances on SCrit, and PCrit, are low in many cases and are lower when the range of tlim is longer. The results of the present theoretical study confirm the possibility of the computation of SCrit and PCrit from several submaximal exercises performed in the same session.
... To date, a continuously debated question is whether W′ determined using a single-visit protocol is affected by previous severe exercise (2)(3)(4)15). Previous research has shown this not to be the case for CP (2,4,6); however, this demonstration is still outstanding for W′. ...
Article
Purpose: This study aimed to assess and compare the systemic response of oxygen uptake kinetics and muscle deoxygenation between a 30-min rest protocol and a multivisit protocol on the parameters of the power-duration relationship (i.e., critical power [CP] and W'). Methods: Nine endurance-trained triathletes reported to the laboratory on five occasions: a preliminary graded exercise test and a familiarization, a 30-min single-visit protocol (time trials of 10, 5, and 2 min in that order interspersed with 30 min rest), and a multivisit protocol (time trials of 10, 5, and 2 min in randomized order interspersed by >24 h rest). Heart rate (HR) was recorded continuously, respiratory gases were measured breath by breath, and deoxygenation was recorded at 10 Hz using near-infrared spectroscopy (NIRS) during all tests. Blood lactate (BLa-) concentration was measured before all time trials. Maximal HR (HRmax), oxygen uptake (V˙O2) during the first 2 min (V˙O2onset), mean response time, end-exercise V˙O2 (V˙O2peak), V˙O2 amplitude (amplV˙O2), O2 deficit, NIRS τ, amplitude (amplNIRS), and time delay were assessed. To compare the two protocols and to assess the differences in W' and CP, a paired sample t-test was used as well as a two-way ANOVA to assess the differences between trials and/or protocols, including trial-protocol interactions. Results: No significant differences, and trivial effect sizes, were found for W' and CP between protocols (P = 0.106-0.114, d < 0.01-0.08). Furthermore, no significant differences between protocols were found for all parameters, except for [BLa-]. Significant differences between trials were found for V˙O2ampl, V˙O2onset, NIRS τ, amplNIRS, [BLa-], and HRmax. Conclusion: Results suggest that W' and CP can be determined using the 30-min rest protocol without confounding effects of previous severe exercise compared with the multivisit protocol.
... To-date, a continuously debated question is whether W´ determined using a single-visit protocol is affected by prior severe exercise (2)(3)(4)15). Previous research has shown this not to be the case for CP (2,4,6), however, this demonstration is still outstanding for W´. ...
... To describe participants' aerobic capacity, a fieldbased critical speed test was performed according to Galbraith, Hopker, Jobson, and Passfield (2011). This test was selected because it was more familiar to our runners compared with laboratory tests, and also because it has been recognised as a good predictor of endurance performance (Galbraith, Hopker, Cardinale, Cunniffe, & Passfield, 2014;Galbraith, Hopker, Lelliott, Diddams, & Passfield, 2014). ...
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The aim of this study was to describe the pacing during a 6-h ultramarathon (race 1) and to investigate whether a slow-start affects performance, running kinematic changes, ratings of perceived exertion (RPE) and fatigue (ROF) (race 2). After a critical speed test, participants completed two 6-h ultramarathons. Race 1 (n = 16) was self-paced, whereas in race 2 (n = 10), athletes performed the initial 36 min at speeds 18% below the mean speed of the initial 36 min of race 1. In race 1, participants adopted an inverse sigmoid pacing. Contact times increased after 1 h, and flight times decreased after 30 min (all P ≤ 0.009); stride length reduced after 1 h 30 min (all P = 0.022), and stride frequency did not change. Despite the lower speeds during the first 10% of race 2, and higher speeds at 50% and 90%, performance remained unchanged (57.5 ± 10.2 vs. 56.3 ± 8.5 km; P = 0.298). However, RPE and ROF were lowered for most of race 2 duration (all P < 0.001). For the comparison of kinematic variables between races, data were normalised by absolute running speed at each time point from 1 h onwards. No differences were found for any of the kinematic variables. In conclusion, decreasing initial speed minimises RPE and ROF, but does not necessarily affect performance. In addition, running kinematic changes do not seem to be affected by pacing manipulation.
... 8,15,18 Finally, a 3-point model provides coaches and practitioners with SEE values, an important measure in assessing the quality of the model and therefore, if possible, a single visit 3-point model is recommended. 19 CS appears to be a reliable and robust parameter with a high level of agreement when transferred from a laboratory to a field setting. 8,19 D' however, has been reported to be less reliable between repeated tests 20 and has shown a lower level of agreement between laboratory and field protocols. ...
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Purpose:: The hyperbolic distance-time relationship can be used to profile running performance and establish critical speed (CS) and D'. Typically, to establish these parameters multiple (3+) performance trials are required, which can be highly fatiguing and limit the usability of such protocols in a single training session. This study aimed to compare CS and D' calculated from a two-trial (2-point model) and a three-trial (3-point model) method. Methods:: 14 male distance runners completed three fixed-distance (3600, 2400, 1200 m) time trials on a 400 m outdoor running track, separated by a 30-minute recovery. Participants completed the protocol nine times across a twelve-month period, with approximately 42-days between each test. CS and D' were calculated using all three distances (3-point model) and also using the 3600 and 1200 m distances only (2-point model). Results:: Mean (±SD) CS for both 3-point and 2-point models was 4.94 ± 0.32 m.s-1, whilst D' was 123.3 ± 57.70 m and 127.4 ± 57.34 m for 3-point and 2-point models, respectively. Overall bias for both CS and D' between 3-point and 2-point model was classed as trivial. Conclusions:: A 2-point time-trial model can be used to calculate CS and D' as proficiently as a 3-point model, making it a less fatiguing, inexpensive and applicable method for coaches, practitioners and athletes to monitor running performance in one training session.
... Cycling: CP -CV(%) = 2-8% W -CV(%) = 7-14 Cycling: CP -CV(%) = 1-7 W -CV(%) = 28(Johnson et al., 2011;Wright et al., 2017) Cycling: CP -CV(%) = 2-3 W -CV(%) = 46 (Experiment 1,Karsten et al., 2015) Running: Critical velocity -CV(%) = < 1-4% D -CV(%) = 9-18%(Galbraith et al., 2011(Galbraith et al., , 2014Nimmerichter et al., 2015) Cycling: CP -CV(%) = 3-4 W -CV(%) = 15-18 (Experiment 3,Karsten et al., 2015) (Continued) Frontiers in Physiology | www.frontiersin.org ...
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Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.
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Purpose: To assess the reliability and construct validity of a self-paced, submaximal run test (SRTRPE) for monitoring aerobic fitness. The SRTRPE monitors running velocity (v), heart rate (HRex), and blood lactate concentration (B[La]), during three 3-minute stages prescribed by ratings of perceived exertion (RPEs) of 10, 13, and 17. Methods: Forty (14 female) trained endurance runners completed a treadmill graded exercise test for the determination of maximal oxygen consumption (VO2max), v at VO2max (vVO2max), and v at 2 mmol·L-1 (vLT1) and 4 mmol·L-1 (vLT2) B[La]. Within 7 days, participants completed the SRTRPE. Convergent validity between the SRTRPE and graded exercise test parameters was assessed through linear regression. Eleven participants completed a further 2 trials of the SRTRPE within a 72-hour period to quantify test-retest reliability. Results: There were large correlations between v at all stages of the SRTRPE and VO2max (r range = .57-.63), vVO2max (.50-.66), and vLT2 (.51-.62), with vRPE 17 displaying the strongest associations (r > .60). Intraclass correlation coefficients (ICC3,1) were moderate to high for parameters v (range = .76-.84), HRex (.72-.92), and %HRmax (.64-.89) at all stages of the SRTRPE. The corresponding coefficients of variation were 2.5% to 5.6%. All parameters monitored at intensity RPE 17 displayed the greatest reliability. Conclusions: The SRTRPE was shown to be a valid and reliable test for monitoring parameters associated with aerobic fitness, displaying the potential of this submaximal, time-efficient test to monitor responses to endurance training.
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For high-intensity muscular exercise, the time-to-exhaustion (t) increases as a predictable and hyperbolic function of decreasing power (P) or velocity (V). This relationship is highly conserved across diverse species and different modes of exercise and is well described by two parameters: the 'critical power' (CP or CV), which is the asymptote for power or velocity, and the curvature constant (W') of the relationship such that t = W'/(P-CP). CP represents the highest rate of energy transduction (oxidative ATP production, V? O2) that can be sustained without continuously drawing on the energy store W' (composed in part of anaerobic energy sources and expressed in kilojoules). The limit of tolerance (time t) occurs when W' is depleted. The CP concept constitutes a practical framework in which to explore mechanisms of fatigue and help resolve crucial questions regarding the plasticity of exercise performance and muscular systems physiology. This brief review presents the practical and theoretical foundations for the CP concept, explores rigorous alternative mathematical approaches, and highlights exciting new evidence regarding its mechanistic bases and its broad applicability to human athletic performance.
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The purpose of this study was to assess the reproducibility of running time to exhaustion (Tlim) at maximal aerobic speed (MAS: the minimum speed that elicits VO2max), on eight subelite male long distance runners (29 +/- 3-yr-old; VO2max = 69.5 +/- 4.2 ml.kg-1.min-1; MAS = 21.25 +/- 1.1 km.h-1). No significant differences were observed between Tlim measured on a treadmill at a 1-wk interval (404 +/- 101 s vs 402 +/- 113 s; r = 0.864); however, observation of individual data indicates a wide within-subjects variability (CV = 25%). In a small and homogenous sample of runners studied, exercise time to exhaustion at MAS was not related to VO2max (r = 0.138), MAS (r = 0.241), running economy (mlO2.kg-1.min-1 at 16 km.h-1) (r = 0.024), or running performance achieved for 3000 m (km.h-1)(r = 0.667). However, Tlim at MAS was significantly related to the lactate threshold determined by the distinctive acceleration point detected in the lactate curve around 3-5 mmol.l-1 expresses in %VO2max (r = 0.745) and to the speed over a 21.1-km race (km.h-1) (r = 0.719). These data demonstrate that running time to exhaustion at MAS in subelite male long distance runners is related to long distance performance and lactate threshold but not to VO2max or MAS.
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The aim of this investigation was to test the hypothesis that a 3-min all-out cycling test would detect a change in critical power (CP) after a 4-wk interval training intervention. Nine habitually active subjects completed a ramp test, two 3-min all-out tests to establish the end power (EP) and the work done above EP (WEP), and three predicting trials to establish CP and W' using the work-time model (W = CPt + W'). After 12 supervised high-intensity interval training sessions over 4 wk, subjects repeated the testing procedures. The CP increased in all subjects after training (pretraining: 230 +/- 53 W; posttraining: 255 +/- 50 W; t8 = 7.47, P < 0.001), with no statistically significant effect on the W' (pretraining: 17.2 +/- 4.2 kJ; posttraining: 15.5 +/- 3.8 kJ; t8 = 2.03, P = 0.08). The all-out test EP was increased after training from 225 +/- 52 W to 248 +/- 46 W (t8 = 6.26, P < 0.001). The EP and CP estimates before and after training were not different and were highly correlated (pretraining: r = 0.96, P < 0.001; posttraining: r = 0.95, P < 0.001). In addition, the increase in EP was correlated with (r = 0.77, P = 0.016) and not different from (t8 = 0.60, P = 0.57) the increase in CP. There was no change in the WEP from pretraining to posttraining (t8 = 1.89, P = 0.10). The present study shows that the 3-min all-out test closely estimates CP across a wide range of aerobic fitness and is sensitive to training-induced changes in CP.
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The present investigation was conducted to determine whether critical power (CP) assesses the ability to perform continuous aerobic exercise and to determine whether training-induced changes in aerobic endurance are reflected by changes in the slope, but not the y-intercept of the CP function. Twelve healthy, active, but untrained male students (mean age +/- SD = 19.1 +/- 0.8 yr) undertook 8 wk of cycle ergometer endurance training (30-40 min a day, three times a week) at an intensity corresponding to their CP. Six control subjects of similar age and initial training status refrained from regular exercise for the same period. Before and immediately following the training period, each of the 18 participants completed three cycle ergometer tests to determine their CP function, an incremental exercise task to establish their maximal oxygen uptake (VO2max), and 40 min of continuous cycle ergometry at or near their calculated CP. CP was significantly correlated with endurance time at 270 W (r = 0.65, P < 0.05) and with the mean power that could be maintained for 40 min (r = 0.87-0.95, P < 0.01), but overestimated the latter by less than 6%. In response to endurance training, CP increased from a mean of 196 +/- 40.9 W to 255 +/- 28.4 W (31%) (ANCOVA, P < 0.01), while the mean power output maintained for 40 min of exercise increased from 190 +/- 34.5 W to 242 +/- 34.9 W (28%). VO2max increased from 49.2 +/- 7.8 ml.kg-1.min-1 to 53.4 +/- 6.4 ml.kg-1.min-1 (8.5%) (P < 0.01).(ABSTRACT TRUNCATED AT 250 WORDS)
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The purpose of this investigation was to determine the contribution of the onset of blood lactate accumulation (OBLA), the heartrate-workload slope (HR-WL) and the efficiency of electrical activity (EEA = slope of IEMG vs. workload) of the leg extensor muscles to Critical Power (CP). Twelve adult males (mean age +/- SD = 24.5 +/- 2.8 yrs) volunteered as subjects for this study. Zero-order correlations indicated that OBLA was significantly (p less than 0.05) related to CP (r = 0.616) and EEA (r = -0.577). Stepwise multiple regression resulted in a one variable model with OBLA the only significant (p less than 0.05) predictor of CP. Furthermore, a related t-test resulted in a significant difference between the means of the power out-put at CP (mean +/- SD = 230.0 +/- 22.1 watts) and OBLA (179.6 +/- 31.8 watts). The results of this study indicated that the two threshold parameters, CP and OBLA, were significantly related and therefore it is likely that the physiological factors responsible for OBLA also influence CP. However, the significant mean differences indicated that the mechanisms which underly CP and OBLA were not identical. Furthermore, the HR-WL slope (mean +/- SD = 0.343 +/- 0.071 beats per watt) and EEA (0.969 +/- 0.572 microvolts per watt) were not potent predictors of CP.
The purposes of this investigation were to determine the validity of critical power (CP) as a measure of the work rate that can be maintained for a very long time without fatigue and to determine whether this corresponded with the maximal lactate steady-state (lass,max). Eight highly trained endurance cyclists (maximal oxygen uptake 74.1 ml.kg-1.min-1, SD 5.3) completed four cycle ergometer tests to exhaustion at pre-determined work rates (360, 425, 480 and 520 W). From these four co-ordinates of work and time to fatigue the regression of work limit on time limit was calculated for each individual (CP). The cyclists were then asked to exercise at their CP for 30 min. If CP could not be maintained, the resistance was reduced minimally to allow the subject to complete the test and maintain a blood lactate plateau. Capillary blood was sampled at 0,5,10,20 and 30 min into exercise for the analysis of lactate. Six of the eight cyclists were unable to maintain CP for 30 min without fatigue. In these subjects, the mean power attained was 6.4% below that estimated by CP. Mean blood lactates (n = 8) reached a steady-state (8.9 mmol.l-1 SD 1.6) during the last 20 min of exercise indicating that CP slightly overestimated lass,max, Individual blood lactates during the last 20 min of exercise were more closely related to the gamma-intercept of the CP curve (r = 0.78, P less than 0.05) than either CP (0.34, NS) or mean power output (r = 0.42, NS).
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The power-endurance time curve described for cycle ergometry has been examined with respect to high velocity treadmill running to exhaustion. On a cycle ergometer, a hyperbolic function has been described that can be linearized by expressing power relative to the inverse of time to exhaustion. The mathematical relationship of this linear function is P = W'/t + theta f, where P = power output, W' = slope of regression, and theta f = fatigue threshold (intercept of power output). Six cross-country runners took part as subjects. Each ran to exhaustion with the treadmill at 6 different velocities between 19.2 and 22.4 km/h. The linear regressions fitted to velocity versus 1/time had correlation coefficients between r = 0.979 and 0.997. It was concluded that the treadmill velocity-endurance time relationship for runs of 2-12 min duration conformed to a similar hyperbolic function as that described for cycle ergometry. The two parameters W' and theta f might provide valuable indices of physical performance potential, which can be used to monitor training responses in competitive runners.