Key words
lactate threshold - time trial - 5000-m - performance - ċLa
max
- V̇O
2max
Abbreviations
ANOVA Analysis of variance
ASR anaerobic speed reserve (v100-V̇O2max)
d Cohen’s d effect-size
HRmax,5k maximal heart rate attained during the 5000-m
time trial
HRmax,RT maximal heart rate attained during the ramp test
protocol
HRmax,ST maximal heart rate attained during the incremental
step test protocol
HRmean,5k mean heart rate attained during the 5000-m time
trial
HROBLA interpolated heart rate corresponding to a lactate
concentration of 4 mmol · l-1 (onset of blood
lactate accumulation, OBLA)
Lamax,ST maximal lactate concentration attained during
the incremental step test protocol
Lapost,5k lactate concentration immediately after performing
the 5000-m time trial
Lapost,RT lactate concentration immediately after performing
the ramp test protocol
p probability of finding the observed (or more
extreme) results when the null hypothesis is
assumed to be true
r correlation coefficient
R² determination coefficient
RE12 interpolated running economy at 12 km · h-1
REOBLA interpolated running economy corresponding to
a lactate concentration of 4 mmol · l-1 (onset of
blood lactate accumulation, OBLA)
RERmax maximal respiratory exchange ratio attained
during the ramp test protocol
t100 time to perform the 100-m all-out sprint
t5k time to perform the 5000-m time trial
TTE time to reach subjective exhaustion during the
ramp test protocol (excluding the time tov
perform the warm-up)
v100 average speed in the 100-m all-out sprint (100/
t100)
v200/v4.8k ratio of the mean velocity during the final 200-m
of the 5000-m time trial and the mean velocity
attained during the prior 4800-m (‘finishing
kick’)
ċLamax maximal lactate accumulation rate
vOBLA interpolated velocity corresponding to a lactate
concentration of 4 mmol · l-1 (onset of blood
lactate accumulation, OBLA)
V̇O2max relative maximal oxygen uptake
vV̇O2max minimal velocity necessary to elicit maximal
oxygen uptake in the ramp test
%V̇O2max fractional utilization of V̇O2max at lactate
threshold according to a fixed lactate concentration
of 4 mmol · l-1 (onset of blood lactate
accumulation, OBLA)
α level of significance
ΔLa100 maximal post-exercise increase in lactate
concentration following the 100-m all-out sprint
test
ΔLaRT increase in lactate concentration during the
course of the ramp test protocol
ΔLa5k increase in lactate concentration during the
course of the 5000-m time trial
Introduction
Knowledge about the determinants of endurance performance and the underlying
metabolic profile is crucial for developing adequate exercise tests, provide
concrete recommendations for improvement and individualise training prescriptions
for athletes. Especially in the field of exercise physiology, various concepts have
been developed to predict endurance performance by means of physiological parameters
[1]. The most common model has been developed by
Michael J. Joyner in 1991, who calculated running speed in the marathon by means of
maximal oxygen uptake (V̇O2max), the fractional utilisation of
oxygen uptake at lactate threshold (%V̇O2max) and running
economy (RE) [2]. Whereas the factors underlying
V̇O2max [3]
[4]
[5] and RE [6]
[7]
[8] have extensively been examined in previous research,
the physiological origin of %V̇O2max (and the
corresponding lactate threshold) remained mostly unknown. As an example, recent
research indicated that the velocity and corresponding
%V̇O2max at lactate threshold do not significantly
correlate [9].
The goal of most lactate threshold concepts is to estimate the maximal lactate
steady-state (MLSS), which is defined as the highest intensity at which lactate
production and clearance are in equilibrium [10]. As
one example, the intensity corresponding to a lactate concentration of
4 mmol·l-1 (vOBLA) has demonstrated a high
correlation and agreement to MLSS [11]
[12]. Joyner & Coyle (2008) updated the existing
model by an anaerobic component and stated “…that truly accurate
models of energy turnover during actual competition would require […]
calculation of fluxes through multiple metabolic pathways (e. g. total
ATP turnover with contributions from both aerobic and anaerobic components
[…])”. However, anaerobic parameters have hardly been
implemented in predictive performance models of long-distance running, which was
recently highlighted in a review article covering a total of 58 studies [13].
A mathematical model designed to describe the regulation of ATP production in muscle
cells was introduced by Alois Mader in 2003 [14]. He
calculated the fractional utilisation of oxidative phosphorylation and glycolysis as
a function of free ADP concentration. Besides V̇O2max, the
maximal rate of glycolysis was included in this model, which is formally known as
maximal lactate accumulation rate (ċLamax). Just recently, the
fundamentals of this concept and the corresponding influence of
ċLamax on MLSS were extensively summarized [15]. Given the same
V̇O2max“[…] Mader’s model predicts
that athletes with a higher [ċLa
max
] generally
have higher lactate concentrations at the same workload [and] reach their [MLSS]
at a lower workload […]
[ 15]. This
indicates that higher values of ċLamax result in a lower
%V̇O2max. Hence, augmenting the metabolic profile by
means of ċLamax might help understand how metabolic processes
interact during exercise, which has direct applications to the individual pacing
strategy [16].
In cycling, previous research demonstrated that mathematical simulation approaches
allow for calculating maximal lactate steady-state with an acceptable reliability
[17]
[18] and
accuracy [19]. Furthermore, test procedures to
determine ċLamax in running have been developed and demonstrated
high reliability [20]
[21]. Usually, the increase in post-exercise lactate concentration
following an 10–15 s all-out sprint test is used to determine
ċLamax. Hence, the required tools exist to examine if
ċLamax is a suitable augmentation of the metabolic profile
and whether it is related to %V̇O2max in running. This
study aims to assess the practical value of ċLamax in terms of
improving performance prediction in endurance running.
Materials and Methods
Participants
A total of N=44 trained endurance athletes (runners n=24;
triathletes n=20) volunteered to participate in this study. As an
inclusion criterion, a 5000-m personal best of 22 and 20 minutes was
required for female (n=15) and male (n=29) participants,
respectively. Participants stated to have an overall weekly training routine and
running distance of 11.9±5.4 h and 56.8±24.2 km,
respectively and had competitive experienced for 8.0±5.8 years ([Table 1]). Prior to any testing, the participants
received a medical check-up based on the guidelines of the European Society of
Cardiology (ESC). This check-up includes notation of the individuals’
account of their own medical, family and personal history, a physical
examination and a resting electrocardiogram [22].
Only participants without positive findings were included. All procedures
received institutional ethics approval (No. 008/2019) according to the
Declaration of Helsinki. Before the investigation, participants were personally
informed about the aims, procedures and potential risks of this study, and gave
their written consent.
Table 1 Descriptive statistics characterising participants and
differences between females and males.
|
Parameter
|
Total
|
Females
|
Males
|
|
|
|
|
(n=44)
|
(n=15)
|
(n=29)
|
d
|
p
|
|
Age
|
[yrs.]
|
25.2±4.1
|
27.1±4.7
|
24.2±3.4
|
0.747*
|
0.028
|
|
Height
|
[m]
|
1.77±0.1
|
1.68±0.06
|
1.81±0.08
|
−1.758***
|
<0.001
|
|
Mass
|
[kg]
|
66.5±9.2
|
58±6.4
|
70.8±7.2
|
−1.843***
|
<0.001
|
|
Body Fat
|
[%]
|
12.5±3.2
|
13.6±4.9
|
11.9±1.7
|
0.539
|
0.202
|
|
Experience
|
[yrs.]
|
8.0±5.8
|
7.5±5.6
|
8.2±5.9
|
−0.121
|
0.756U
|
|
Training
|
[h·wk-1]
|
11.9±5.4
|
12.3±4.5
|
11.6±5.8
|
0.130
|
0.683
|
|
Training
|
[km·wk-1]
|
56.8±24.2
|
62.2±12.0
|
54.1±28.4
|
0.332
|
0.307
|
Values are expressed as mean value (x̄) and standard deviation (SD).;
* significant difference between female and male participants
(p≤0.05); *** significant difference between
female and male participants (p≤0.001); U Comparisons
between female and male participants were performed by using the
Mann-Whitney-U test.
Design
The design of this study oriented on recent research that examined the
physiological determinants of ultramarathon trail-running [23]
[24]. Measurements
were performed from March to September 2019. The participants had to perform
various exercise tests including an incremental step test, a 100-m all-out
sprint test, a ramp test and a 5000-m time trial. Tests were performed within
one week on Mondays, Wednesdays and Fridays to ensure agreement between
physiological and performance related parameters while minimising fatigue
between procedures. All participants were instructed to refrain from caffeinated
beverages for at least 8 h before testing and to avoid any vigorous
physical activity on the testing day and the day before. For gas exchange
analyses in the laboratory, participants had to arrive in a carbohydrate-loaded
state, but with a fasting period of at least two hours. To avoid influences
based on circadian rhythm, the participants performed the laboratory tests on
Mondays and Wednesdays at approximately the same time of the day.
On their first visit to the laboratory, the participants were informed about the
procedures, received the medical check-up and underwent a ten-site skinfold
thickness measurement (Harpenden Skinfold Caliper, Baty Int., West Sussex,
United Kingdom) to determine their body fat percentage [25]. Afterwards, the participants performed an incremental step test
on a treadmill. On Wednesdays, the participants performed the 100-m all-out
sprint test on an indoor track and approximately one hour afterwards a ramp test
protocol until subjective exhaustion in the laboratory. On their last visit
(Fridays), the participants performed a 5000-m time trial on a 400-m outdoor
track. Testing in the laboratory was performed on a motorised treadmill (saturn
300/100, h/p/cosmos sports & medical GmbH,
Nussdorf-Traunstein, Germany) with a constant gradient of 1% [26]. According to the guidelines of the
manufacturer, the participants wore a safety belt, which was connected to the
automatic security brake system of the treadmill.
Protocols
Incremental step test
The incremental step test started with an initial velocity of
2.0 m·s-1
(7.2 km·h-1 or
8:20 min·km-1), which increased by
0.4 m·s-1
(1.44 km·h-1) every five minutes as
illustrated in [Fig. 1a]. At the end of every
step, the treadmill stopped for 30 seconds in which ratings of
perceived exertion [27] were noted and a blood
sample (20 μl) was collected from the right earlobe to
determine lactate concentration immediately (Biosen C-Line, EKF-diagnostic
GmbH, Barleben, Germany). The incremental step test was terminated when
blood lactate concentration exceeded
4.0 mmol·l-1. Fractional utilization of
V̇O2max (%V̇O2max) at
lactate threshold was interpolated for the velocity according to a fixed
lactate concentration of 4 mmol·l-1
(vOBLA). Throughout the incremental test, participants wore
an airtight silicone oro-nasal mask (7450 Series, V2™, Hans-Rudolph,
Inc., Shawnee, KS, United States of America) to record oxygen uptake
(V̇O2) and carbon dioxide output
(V̇CO2) breath-by-breath by a spirometric device (ZAN
600 USB, nSpire Health, Inc., Longmont, CO, United States of America). Flow
sensors were calibrated manually by using a standardised 3000 ml
high precision syringe (nSpire Health, Inc., Longmont, CO, United States of
America). Gas concentration was calibrated under laboratory conditions as
well as a gas mixture of 15% O2 and 6%
CO2.
Protocols used for the incremental step test (left) and the ramp test (right).; Occasions for blood sampling to determine lactate concentration
are marked by the drop symbol. During the incremental test, gas analyses were provided throughout, whereas the 8 min warm-up preceding
the ramp test was performed without gas analyses.
100-m all-out sprint test
The participants performed the 100-m all-out sprint test and the standardised
warm-up of 15 minutes including technical drills and starts as
described previously [20]
[21]. Throughout the sprints, participants were
verbally encouraged by the examiners. The time to cover the 100 metres
(t100) was determined using a start pedal and a double
infrared photoelectric light barrier (Sportronic Electronic Sports
Equipment, Winnenden-Herthmannsweiler, Germany). Blood samples were
collected immediately before and after the sprint test, as well as every
minute after the sprint for 10 minutes.
ċLamax was calculated as the difference between the
measured maximal post-exercise lactate concentration and resting lactate
concentration (ΔLa100), which was divided by the
difference between t100 and the period at the beginning of
exercise for which no lactate formation is assumed (talac) (Eq.
1) [20]
[21]
[28]
[29].
As a representation of phosphocreatine metabolism, talac was
interpolated according to previous research (Eq. 2) [20]
[28].
After the last blood sample was collected, participants performed an
individual cool-down for 10-min at a self-determined intensity and had to
arrive at the laboratory approximately 45 min afterwards.
Ramp test
As a warm-up preceding the ramp test protocol, the participants performed
eight minutes at 2.8 m·s-1
(10.08 km·h-1 or
5:57 min·km-1) without spirometric
measurement ([Fig. 1b]). After a short break
for attaching the mask, the ramp test protocol started with an initial
velocity of 2.8 m·s-1 for another
2 minutes. Afterwards, velocity increased by
0.15 m·s-1
(0.54 km·h-1) every 30 seconds until
subjective exhaustion of the participants. The time to exhaustion (TTE) was
noted. Data of gas analysis were averaged for every single 30 second
step to determine V̇O2max. The minimal velocity necessary
to elicit maximal oxygen uptake (vV̇O2max) was determined
as the velocity corresponding to the highest value for oxygen uptake.
Anaerobic speed reserve (ASR) was calculated as the difference between the
average speed during the 100-m all-out sprint (v100) and
vV̇O2max. Blood lactate concentration was determined
before and after performing the warm-up as well as immediately after the
ramp test protocol. As criteria for V̇O2max, a plateau
of≤150 ml·min-1, a heart rate
of≥95% HRmax (highest values attained in the ramp
test or 5000-m time trial), a respiratory exchange ratio (RER)
of≥1.05 and a post-exercise lactate concentration
of≥8 mmol·l-1 were used for
evaluation [30]. After the ramp test,
participants were encouraged to perform an individual cool-down at a
self-determined intensity and duration.
5000-m time trial
The participants started with an easy jog for 10 min at a
self-determined intensity on the 400-m track. Afterwards, the participants
performed various technical drills for approximately 7 to
10 minutes, followed by four ascending runs of approximately 50
metres. After performing the warm-up, participants had a passive recovery of
5 to 10 minutes before performing the 5000-m time trial. The 5000-m
time trial started simultaneously for all participants that had been tested
in the respective week (2 to 6 participants). The examiner gave the
following instruction regarding the participants’ choice of pacing
strategy:
“Try to finish the 5000-m in the shortest time possible. This
should be the main goal for your attempt. At the end of the race, you
should arrive with nothing left in the tank. You can freely choose and
adjust your individual pacing strategy. Don’t let yourself be
distracted by the pace of the other runners. But try to increase your
velocity predominantly towards the end of the race.”
Throughout the time trial, the participants wore a sport watch (Garmin
Forerunner 920XT, Garmin International, Inc., Olathe, KS, United States of
America), which recorded the participants’ time, heart rate, cadence
and pace. Participants were allowed to take a look at these measures ad
libitum. Split times were hand-stopped by one examiner for each runner, who
were standing inside the 400-m track near the finish line. To accurately
record the participants’ 200-m splits, two markers (javelins with
flashy pennants at the top) were placed near the beginning of the curves as
illustrated in [Fig. 2]. Participants
received verbal feedback every second 200-m split time by their examiner.
Additionally, feedback about the remaining laps was given for the last four
laps. As a measure of the ‘finishing kick’, the average
velocity during the final 200 m was divided by the average velocity
of the preceding 4800 m (v200/v4.8k).
Lactate concentration was determined before and after the warm-up, as well
as immediately before and after performing the 5000-m time trial.
Schematic illustration of the realisation of 5000-m time trials.; 2–6 individuals (depending of the respective week) started simultaneously
at the starting line and performed their individual race to accomplish the 12 ½ rounds in the shortest time possible. The examiners (one examiner
per participant) stood near the finish line and measured every single 200-m split resulting in a total of 25 split times. To improve the accuracy of split
times, two split markers (javelins) were placed in front of the corners.
Statistical analyses
Statistical analyses were done using SPSS (25, IBM SPSS, Armonk, NY, USA). To
access which physiological variables significantly predict 5000-m time trial
performance (t5k), a stepwise multiple regression analysis was
performed. This analysis is in line with previous research focussing on
ultramarathon trail-running [23]
[24]. However, this study excluded anthropometrics
and performance parameters and focussed on purely physiological variables.
Physiological variables were entered into the model if there was a significant
change in the F-value (p≤0.05) and by order of their change in
R2. The assumptions of normality, linearity and homoscedasticity
were checked visually by using the plot of expected cumulated probability
against observed cumulative probability (P-P plot) and the plot of standardized
residuals (ZRESID) against standardised predicted values (ZPRED). Independence
of errors was assessed by Durbin-Watson statistics (ranging between 0 and 4)
with a value of close to two indicating that the residuals are uncorrelated.
Collinearity statistics were calculated as tolerance and variance inflation
factor (VIF).
Differences between female and male runners were analysed by using independent
t-tests in case of normally distributed values in both groups or by using the
non-parametric Mann-Whitney U-test. Normality was checked by using the
Shapiro-Wilk test (α>0.10), since it is more appropriate for
small sample sizes (N≤50) and more powerful when compared to the
Kolmogorov-Smirnov test (even with Lilliefors correction) [31]
[32]
[33]. Analogously, a dependent t-test or
Wilcoxon’s test were applied for analysing differences between maximal
heart rate attained in the ramp test (HRmax,RT) and during the 5000-m
time trial (HRmax,5k). The individual differences between maximal
heart rates were examined visually. As a measure of effect-size, Cohen’s
d was calculated. Correlation analyses were performed for
ċLamax with %V̇O2max,
v200/v4800 and ASR by using Pearson’s
correlation coefficient or alternatively by Spearman’s rank correlation
in case of significant violations to normal distribution.
Results
A total of n=43 participants performed all exercise tests of this study. One
participant attained a calf-muscle strain, which is why data for the 5000-m time
trial are missing for this participant. Due to the personal schedule of the
participants, the 5000-m time trial was, in few cases (n=3), delayed for one
week. Since testings were performed from March to September, ambient temperature
during the 5000-m time trials ranged between 10° and 26°C with
drizzling rain on two occasions.
The V̇O2max criteria
of≤150 ml·min-1 plateau and a heart rate
of≥95% HRmax were met for almost all (except two)
participants (96%). The criteria for RER≥1.05 and lactate
concentration≥8 mmol·l-1 were met by 64 and
39% of the participants, respectively. In total, the participants met at
least four (30%), three (68%), two (96%) or one
(100%) of these V̇O2max criteria. All participants stated
to be exhausted at the end of the test.
Maximal heart rate did not significantly differ between values attained during the
ramp test and the 5000-m time trial (d=0.015, p=0.616). However, a
large variation in individual heart rate differences was observed ([Fig. 3a]). Whereas 18% of the participants
attained exactly the same value for maximal heart rate during the ramp test and time
trial, 47% attained a higher value during the ramp test ([Fig. 3b]). However, only half of these individuals
demonstrated a difference that exceeded 3 min-1. On the other
hand, 35% of the participants attained a higher maximal heart rate during
the time trial with 18% of all participants demonstrating a difference of at
least 5 min-1.
Maximal heart rate attained during the ramp test (RT) and 5000-m time trial (TT).; Mean (columns with standard deviation) and individual
(lines) comparison between maximal heart rate attained during TT and RT (d = 0.015, p = 0.616).; Distribution of the difference between maximal
heart rate (TT-RT). The unit of the absolute differences is min-1. The grey area indicates that maximal heart rate was equal in TT and RT. The blue
areas indicate that maximal heart rate was higher during TT compared to RT. Red areas indicate that maximal heart rate was higher during RT compared
to TT.
The participants performed the 5000-m time trial at a high percentage of their
maximal heart rate, which exceeded 90% for most of the time ([Fig. 4]). Individual and mean pacing characteristics
during the time trial demonstrate a fast start and a high variability at the end.
Some participants performed the finish with a very high increase in velocity, while
others demonstrated a steadier pace. Cadence demonstrated a high variability between
participants and was highest during the start and finish of the race.
Heart rate, velocity and cadence during the course of the
5000-m time trials.; Thick black lines represent the mean values over
all participants. Thin coloured lines represent individual values of all
participants. Heart rate is expressed as a percentage of maximal heart
rate (HRmax). Velocity is expressed as a percentage of mean 5000-m
velocity (representing 100 %). Cadence was determined by the watch
the participants were wearing (Garmin Forerunner 920XT).
Stepwise multiple regression demonstrated that augmenting Joyner’s model
(V̇O2max, REOBLA and
%V̇O2max) by means of ċLamax
explained an additional amount of variance
(ΔR2=4.4%, p=0.006) in t5k
resulting in a total R2 of 79.8% (see Supplementary Table).
Durbin-Watson statistics resulted in a value of 2.058. Visually examination of the
respective plots demonstrated that the criteria for normality, linearity and
homoscedasticity were met by the final model (see Supplementary Figure). Tolerance
and VIF of the included parameters ranged from 0.627 to 0.777 and 1.332 to 1.595,
respectively ([Table 2]). V̇O2max
demonstrated the highest standardized coefficient (β=−0.978)
while ċLamax showed the lowest value
(β=−0.244). However, performing the same analysis
exclusively for males, ċLamax was not included in stepwise linear
regression.
Table 2 Physiological predictors of multiple regression
associated with 5000-m time trial performance.
|
Predictors
|
b
|
SE
|
β
|
T
|
p
|
Tolerance
|
VIF
|
|
Intercept (constant)
|
36
|
2.895
|
|
12.544
|
<0.001
|
|
|
|
V̇O2max
|
−0.264
|
0.025
|
−0.978
|
−10.619
|
<0.001
|
0.627
|
1.595
|
|
REOBLA
|
0.042
|
0.008
|
0.423
|
5.112
|
<0.001
|
0.777
|
1.287
|
|
%V̇O2max
|
−0.101
|
0.023
|
−0.364
|
−4.320
|
<0.001
|
0.751
|
1.332
|
|
ċLamax
|
−2.246
|
0.778
|
−0.244
|
−2.888
|
0.006
|
0.743
|
1.347
|
b=unstandardized coefficients;
ċLamax=maximal lactate accumulation rate;
p=probability of finding the observed (or more extreme) results
when the null hypothesis is assumed to be true;
REOBLA=interpolated running economy corresponding
to a lactate concentration of 4 mmol·l-1
(onset of blood lactate accumulation, OBLA); SE=standard error
of unstandardized coefficients; T=empirical value of
t-statistics; VIF=variance inflation factor;
β=standardized coefficients,
Female participants demonstrated a lower body mass, lower height and higher age
([Table 1]). Body fat percentage, as well as
training experience and volume did not differ between females and males. Regarding
performance variables, females had a slower t100 and t5k
([Table 3]). Females demonstrated a lower TTE,
vV̇O2max, V̇O2max,
ċLamax, ASR and v200/v4.8k.
During the ramp test protocol, females demonstrated a lower maximal RER and a lower
post-exercise lactate concentration. The lactate concentrations following the sprint
test and the 5000-m time trial were also lower in females. No significant
differences between females and males could be found in heart rate parameters.
ċLamax significantly correlated with ASR (r=0.644,
p<0.001), %V̇O2max (r=−0.439,
p=0.003) and v200/v4.8k (r=0.389,
p=0.010) ([Fig. 5]).
Relationships between maximal lactate accumulation rate
(ċLamax) and other physiological and performance parameters.; a)
Correlation between ċLamax and the fractional utilisation of V̇O2max
at lactate threshold ( %V̇O2max) according to a fixed lactate concentration
of 4 mmol · l-1.; b) Correlation between ċLamax and the finishing
kick during the 5000-m time trial. The finishing kick is expressed
as the ratio between the average velocity during the last 200-m of
the 5000-m time trial and the average velocity during the preceding
4800 metres (v200/v4.8k).; c) Correlation between ċLamax and the
anaerobic speed reserve (ASR). ASR was calculated as the difference
between the average speed during the 100-m all-out sprint (v100)
and the minimal velocity necessary to elicit maximal oxygen uptake
(vV̇O2max).; All relationships attained statistical significance with a)
p = 0.003 b) p = 0.010 and c) p ≤ 0.001.
Table 3 Physiological and performance parameters of female and
male participants.
|
Parameter
|
Total
|
Female
|
Male
|
|
|
|
|
(n=44)
|
(n=15)
|
(n=29)
|
d
|
p
|
|
V̇O2max
|
[ml·min-1·kg-1]
|
60.5±5.7
|
55.4±3.9
|
63.2±4.5
|
−1.810***
|
<0.001U
|
|
vV̇O2max
|
[m·s-1]
|
5.17±0.4
|
4.8±0.26
|
5.36±0.31
|
−1.903***
|
<0.001U
|
|
vOBLA
|
[m·s-1]
|
4.08±0.36
|
3.88±0.16
|
4.18±0.4
|
−0.884**
|
0.003U
|
|
%V̇O2max
|
[%]
|
85.9±5.4
|
87.6±6
|
85±4.9
|
0.491
|
0.243U
|
|
REOBLA
|
[ml·kg-1·km-1]
|
213±15
|
208±11
|
215±17
|
−0.459
|
0.162
|
|
RE12
|
[ml·kg-1·km-1]
|
214±18
|
209±14
|
217±19
|
−0.457
|
0.220
|
|
t100
|
[s]
|
13.9±1.35
|
15.39±1.14
|
13.14±0.58
|
2.775***
|
<0.001U
|
|
ASR
|
[m·s-1]
|
2.08±0.50
|
1.73±0.43
|
2.27±0.44
|
−1.237***
|
<0.001
|
|
ċLamax
|
[mmol·l-1·s-1]
|
0.67±0.16
|
0.55±0.13
|
0.74±0.14
|
−1.389***
|
<0.001
|
|
t5k
|
[min]
|
19.05±1.51
|
20.43±1.02
|
18.31±1.18
|
1.880***
|
<0.001
|
|
v200/v4.8k
|
[%]
|
110.9±9.7
|
105.9±8.4
|
113.6±9.4
|
−0.849*
|
0.011
|
|
RERmax
|
|
1.12±0.2
|
1.05±0.05
|
1.16±0.23
|
−0.579**
|
0.006U
|
|
TTE
|
[min]
|
8.43±1.35
|
7.1±0.85
|
9.12±1
|
−2.120***
|
<0.001U
|
|
HRmax,ST
|
[min-1]
|
187±10
|
186±13
|
187±9
|
−0.095
|
0.766
|
|
HRmax,RT
|
[min-1]
|
191±9
|
188±12
|
193±7
|
−0.557
|
0.114
|
|
HRmax,5k
|
[min-1]
|
192±9
|
189±11
|
193±7
|
−0.466
|
0.242
|
|
HRmean,5k
|
[min-1]
|
179±10
|
175±13
|
181±8
|
−0.600
|
0.110
|
|
HROBLA
|
[min-1]
|
181±10
|
180±12
|
181±8
|
−0.105
|
0.655
|
|
HROBLA
|
[%HRmax]
|
93.6±2.8
|
94.5±2.5
|
93.1±2.8
|
0.518
|
0.090U
|
|
HRmean,5k
|
[%HRmax]
|
92.8±2.5
|
92.1±3.3
|
93.2±2.5
|
−0.322
|
0.476U
|
|
Lamax,ST
|
[mmol·l-1]
|
6.04±1.29
|
6.18±1.21
|
5.97±1.35
|
0.161
|
0.512U
|
|
Lapost,RT
|
[mmol·l-1]
|
7.15±1.76
|
6.42±1.99
|
7.52±1.53
|
−0.648*
|
0.026
|
|
Lapost,5k
|
[mmol·l-1]
|
8.48±2.34
|
7.51±1.92
|
9.01±2.4
|
−0.667*
|
0.044
|
|
ΔLa100
|
[mmol·l-1]
|
6.99±1.23
|
6.43±1.25
|
7.27±1.14
|
−0.713*
|
0.029
|
|
ΔLaRT
|
[mmol·l-1]
|
5.87±1.74
|
5.31±2.06
|
6.16±1.5
|
−0.498*
|
0.045
|
|
ΔLa5k
|
[mmol·l-1]
|
6.49±2.18
|
5.87±1.97
|
6.82±2.24
|
−0.442
|
0.177
|
Values are expressed as mean value (x̄) and standard deviation (SD).;
* significant difference between female and male participants
(p≤0.05); ** significant difference between
female and male participants (p≤0.01); ***
significant difference between female and male participants
(p≤0.001); U Comparisons between female and male
participants were performed by using the Mann-Whitney-U test.;
ASR=anaerobic speed reserve which was calculated as the difference
between the average speed during the 100-m all-out sprint and the minimal
velocity necessary to elicit maximal oxygen uptake;
ċLamax=maximal lactate accumulation rate;
d=Cohen’s d effect-size; HRmax,5k=maximal
heart rate attained during the 5000-m time trial;
HRmax,RT=maximal heart rate attained during the ramp test
protocol; HRmax,ST=maximal heart rate attained during the
incremental step test protocol; HRmean,5k=mean heart rate
attained during the 5000-m time trial;
HROBLA=interpolated heart rate corresponding to a lactate
concentration of 4 mmol·l-1 (onset of blood
lactate accumulation, OBLA); Lamax,ST=maximal lactate
concentration attained during the incremental step test protocol;
Lapost,5k=lactate concentration immediately after
performing the 5000-m time trial; Lapost,RT=lactate
concentration immediately after performing the ramp test protocol;
p=probability of finding the observed (or more extreme) results when
the null hypothesis is assumed to be true;
RE12=interpolated running economy at
12 km·h-1;
REOBLA=interpolated running economy corresponding to a
lactate concentration of 4 mmol·l-1 (onset of
blood lactate accumulation, OBLA); RERmax=maximal
respiratory exchange ratio attained during the ramp test protocol;
t100=time to perform the 100-m all-out sprint;
t5k=time to perform the 5000-m time trial;
TTE=time to reach subjective exhaustion during the ramp test
protocol (excluding the time to perform the warm-up);
v200/v4.8k=ratio of the mean
velocity during the final 200-m of the 5000-m time trial (‘finishing
kick’) and the mean velocity attained during the prior 4800-m;
vOBLA=interpolated velocity corresponding to a
lactate concentration of 4 mmol·l-1 (onset of
blood lactate accumulation, OBLA);
V̇O2max=maximal oxygen uptake;
V̇O2max=minimal velocity necessary to elicit
maximal oxygen uptake;
%V̇O2max=fractional utilization of
V̇O2max at lactate threshold according to a fixed
lactate concentration of 4 mmol·l-1 (onset of
blood lactate accumulation, OBLA); ΔLa100=maximal
post-exercise increase in lactate concentration following the 100-m all-out
sprint test ; ΔLaRT=increase in lactate
concentration during the course of the ramp test protocol;
ΔLa5k=increase in lactate concentration
during the course of the 5000-m time trial.
Discussion
The aim of this study was to assess the practical value of ċLamax
in terms of improving performance prediction in a 5000-m time trial. It was found
that including ċLamax in a model to calculate 5000-m time trial
performance allows to explain a significant amount of variance
(+4.4%) in a mixed-sex group of trained athletes. Females had a
slower t5k and a lower V̇O2max,
ċLamax, ASR and v200/v4.8k
compared to males. Furthermore, ċLamax demonstrated a
significantly negative correlation with %V̇O2max and a
positive correlation with v200/4.8k and ASR. Additionally,
maximal heart rate showed high inter-individual differences between the ramp test
and the 5000-m time trial.
The most important physiological variables to explain 5000-m time trial performance
were, in descending order, V̇O2max, REOBLA,
%V̇O2max and ċLamax. This model
sufficiently met the assumptions of normality, linearity, homoscedasticity,
non-collinearity and independence of errors indicating adequate dependability of the
results. It is important to note that this information has poorly been reported in
previous models [13]. In fact, more than 66%
of the variance in 5000-m time trial performance could be explained by
V̇O2max and REOBLA. An explanation of nearly
80% of the variance in t5k seems to be rather small when compared
to other predictive performance models described in the literature [13]
[23]
[24]. However, most of these models include parameters
that can be characterised as being both, physiological and performance parameter.
For example, vV̇O2max (or maximum velocity in a ramp test) is one
of the major variables associated with 5000-m [13] and
50-km performance [23]. The same applies to this very
study: vV̇O2max would have predicted 5000-m time trial
performance to a high extend. Including this parameter in our model would have
resulted in two problems. Firstly, vV̇O2max demonstrates a high
correlation to V̇O2max and as such would have increased
collinearity. Secondly, this parameter is highly related to the ability to sustain
an increasing task until exhaustion [34]. As such,
this can be characterised as a kind of performance test that requires anaerobic
capabilities as well. In order to assess relevant predictors for 5000-m performance,
we decided to implement a purely physiological model. Pastor et al. (2022)
demonstrated that 100-km performance was associated with muscular strength and body
composition and that longer distances seem to lack prediction by classical
physiological variables [24]. As highlighted in their
conclusions, the implementation of other variables raleted to (neuro-)muscular
fatigue might have improved performance prediction. This is in line with the
upcoming concept of ‘durability’ which was recently highlighted
[35].
The significant correlation between ċLamax and
%V̇O2max indicates a qualitative agreement with the
assumptions of Alois Mader [14]. Participants with a
higher ċLamax demonstrate a lower
%V̇O2max (given a similar V̇O2max)
[15]. However, given a variance explanation of
20% and the rather high variability, this finding should not be overrated.
It is important to note that this is a first approximation to the way more complex
interdependencies described in Mader’s model [14]
[15]
[28].
As in other scientific contexts, the correlation between ċLamax
and %V̇O2max does not imply causation. Longitudinal
studies should augment ċLamax in exercise testing and examine,
whether a change in ċLamax is related to a change in
%V̇O2max. This could verify the assumption made in
this cross-sectional investigation. Another assumption of this model, that
%V̇O2max increases with higher values of
V̇O2max [15], could not be
verified with the data of this study. In fact, the correlation of these parameters
even tended to be negative. Hence, it should be examined what kind of model
– other than a pure linear one as applied here – might be the most
adequate to describe the relationship between these measures.
Moreover, blood lactate concentration depends on the rate of release and removal, as
well as the distribution volume [36]. Medbø
& Toska (2001) examined post-exercise lactate concentration following (1-)
2 min of (non-) exhaustive bicycling. They found that the estimated
distribution volume changes increases over time and is significantly larger
following non-exhaustive exercise when compared to exhaustive cycling from
3 min after exercise onwards [36]. Hence,
actual values of ċLamax might be underestimated by using net
post-exercise lactate concentration and assuming a constant distribution volume.
However, since this study applied a completely different type of exercise
(~14-s all-out), we can only speculate about the transferability of these
findings. Recent research demonstrated that the interpretation of velocity constants
describing lactate exchange and removal should consider the applied modelling
approach as well as exercise intensity and duration [37].
Differences between female and male in sprint and endurance performance as well as
V̇O2max agreed with the literature [38]. %V̇O2max and RE were similar between
sexes, which might be due to the trained performance level of the participants [39]. The difference in ċLamax
between sexes is influenced by the mathematical dependence on t100, which
was considerably higher in females. Despite the significantly longer exercise time,
the pure increase in post-exercise lactate concentration following the sprint was
found to be lower in females. This indicates that the net glycolytic power is lower
in females, which might result from differences in muscle mass and fibre size
contribution.
The positive correlation between ċLamax and
v200/v4.8k indicates that the athletes with a
higher glycolytic power are capable of performing an even more reinforced
‘finishing kick’. This seems reasonable since spurts of higher
intensity put substantial demands on glycolysis in terms of substrate-level
phosphorylation. This could have direct applications to athletic practice and the
individual racing strategy. However, ċLamax only explained
15% of the variance found in v200/v4.8k making
it hard to provide concrete recommendations. Aside from physiological factors, the
anticipatory feedback model also considers psychological and environmental factors
to explain modifications in work rate during time trials [16]. In this context, the rate of lactate production and concomitant
physiological changes in muscular pH might be potent afferent feedback to optimise
individual pacing.
Maximal heart rate assessment appears to be influenced by various factors resulting
in rather high inter-individual differences. In contrast to the findings of this
study demonstrating similar average values for maximal heart rate during the time
trial and ramp test (192±9 vs. 191±9 min-1,
respectively), previous research indicated that maximal heart rate is substantially
higher during training and competition (>10 min-1) when
compared to a graded exercise test [40]. The
discrepancy between studies might result from differences in exercise protocols. A
reason for the higher maximal heart rate attained during the time trial when
compared with the ramp test could be phenomenon called cardiovascular drift [41]. Cardiovascular drift is characterised as decrease
in stroke volume and concomitant increase in heart rate during prolonged aerobic
exercise, which might result from an increase in body temperature [42]. Accordingly, previous research demonstrated that
heart rate during ramp tests designed to quickly elicit V̇O2max
might not result in true maximal heart rate when compared to other lab tests, field
tests or even competitions of longer duration [43]
[44]. The same holds for the huge
inter-individual variation in maximal heart rate differences between procedures as
observed in this study [44]. Given that heart rate
increases with ambient temperature, the conditions during the individual time trials
might influence the differences seen in maximal heart rate [45]. Another reason for a higher peak heart rate during the time trial
could be the difference in the preceding warm-up [43].
In the study of Ingjer (1991), nine out of ten participants demonstrated a higher
peak heart rate after performing a 30 min warm-up at 60%
V̇O2max when compared to a 10 min warm-up at the same
intensity. This could again be influenced by the phenomenon of cardiovascular drift
[41]. However, given the fact that the warm-ups
differed in other factors as well (e. g. technical drills and ascending runs
compared to steady running at low-intensity), a direct comparison seems to be
challenging. A higher maximal heart rate attained during the ramp tests could result
from a fatigue effect occurring for exhaustive exercise when performed on
consecutive days [43]. However, given that there was
one day of rest between ramp test and the time trial, this influence might be less
in this study. Additionally, receiving verbal encouragement throughout the ramp test
(as opposed to the time trial in which encouragement was present every 200 to 400
metres) might increase the participants’ motivation to perform well and thus
result in higher values of maximal heart rate [46].
However, the same effect was found for head-to-head competitions that were simulated
during the time trials. We assume that this effect is moderated by the similarity of
the participants’ performance and pacing in the respective time trial
resulting in a literally more or less head-to-head competition.
Limitations
A very important aspect worth considering in this study is the fact that multiple
regression was applied in a mixed-sex group of endurance athletes. Since females
had a significantly lower t5k and ċLamax, the
significant inclusion of ċLamax might (at least in parts) be
influenced by the effect of sex. However, subgroup analyses would have lacked
statistical power for multiple regressions covering four predictors for the
given effect size. Future studies are encouraged to replicate this study in a
larger sample of females or males.
Differing conditions during time trials (e. g. temperature, wind and
weather) might influence the agreement between laboratory findings and endurance
performance and pacing in the field and the comparison between individual
participants. However, previous research comparing a wide range of temperatures
(-14 to+20°C) at a wind speed of
5 m·s-1 did not find an effect on TTE, RE and
V̇O2max in female endurance runners [45]. Hence, we believe that the potential
perturbations in time trial resulting from differences in outdoor conditions are
in the range of day-by-day variability and do not substantially influence the
findings of this study.
Even though %V̇O2max based vOBLA as a
frequently used lactate threshold of 4 mmol·l-1
[11], there is reason to debate why we did not use
other (ventilatory) thresholds instead. Some researchers argue that lactate and
ventilatory thresholds can be seen as surrogates in cycling [47] and running [48],
since both concept result in similar intensities and demonstrate a similar
degree of reliability [49]
[50]. However, other studies highlight the caveats of ventilatory
thresholds in terms of objectivity and reliability [51]
[52]. Since both approaches seem to
be equally effective for estimating the beginning of the high-intensity domain
in terms of MLSS [53], and the fact that
vOBLA was equally reliable when compared to
‘individual’ lactate thresholds in cycling [54], we feel that the applied lactate threshold is
an adequate measure. However, especially in the context of mathematical
simulation approaches – as already conducted in cycling – one
should be aware of the potential differences between
%V̇O2max at MLSS and vOBLA in the
outcomes. To overcome this bias, future research aiming to validate simulation
approaches in running should determine %V̇O2max as
the V̇O2 measured in a continuous trial at MLSS velocity.
Despite stepwise linear regression analyses are frequently used, they have been
criticised for including nuisance variables that reduce the out-of-sample
accuracy [55]. It was stated that the probability
of including nuisance variables increases with the number of potential
predictors (candidates) [55]. The number of
candidates used in our study was even lower than the lowest count applied in
Monte Carlo simulation. Hence, we argue that the low number of candidates
applied in this study reduces the risk for including nuisance variables.
However, recently published studies examining the determinants of Ultramarathon
Trail-running performance applied stepwise multiple regression analyses without
discussing methodical caveats [23]
[24].
Maximum rate of glycolysis in terms of ċLamax was derived from
post-exercise lactate concentrations following a 100-m sprint. [20]
[21]. One might
argue that the applied exercise duration (10–15 s) is too short
for inducing high reading of lactate concentration and that a 30-s Wingate is
more suitable for achieving this [56]
[57]
[58]. However,
given that glycolysis increasingly inhibits its key enzyme phosphofructokinase
(due to a concomitant reduction intracellular pH) [59], applying exercises beyond 15 s would lead to premature
fatigue and, as such, impede to determine the maximal rate of glycolysis.
This phenomenon has been demonstrated and described in recent investigation
[60] and in a narrative review [15]. Since recent research indicates that
ċLamax is sport-specific as it does not correlate between
cycling and running [21], the application of a
short all-out test in running seems to be applicable here.
The significant correlation between ċLamax and ASR are
influenced by the mathematical dependence on t100, which is used in
the calculation of both parameters. However, even pure ΔLa100
explained almost 22% of the variance ASR. It is important to note that
ASR was calculated by using the mean and not maximal velocity attained during
the 100-m sprint. Hence, absolute values of ASR are not comparable to other
research indicating considerably higher values [61]
[62]
[63]. Based on the correlation between t100 and maximal
sprint speed, future studies might apply corresponding relationships to
re-calculate the values in this study [61].
Conclusion
The present findings indicate that ċLamax allows for significant
(yet minor) improvements in 5000-m performance prediction in a mixed-sex group and
it is related to %V̇O2max and the ‘finishing
kick’. This expands the established performance models by means of an
anaerobic capability, which is relevant for understanding exercise physiology and
performance. Since ċLamax testing is a time-efficient procedure
and does not restrict the athletes’ training schedule, scientists and
coaches are encouraged to implement it in practice. Future studies need to replicate
this analysis in middle-distance events and examine differences in their
predictability. Longitudinal studies examining the effects of deliberate training on
ċLamax are sparse and thus of particular interest.
Athletes aiming to improve their ‘finishing-kick’ might need to
increase their ċLamax in order to provide the required power of
glycolytic metabolism. However, since this study investigated pacing only in terms
of the ‘finishing kick’, future research should identify pacing
strategies over the complete time trial distance. Such pacing clusters could be
compared by means of their performance outcomes and physiological characteristics to
improve the understanding of individual pacing in running. Athletes aiming to elicit
HRmax should be aware of the high inter-individual differences
between procedures, which directly affect training prescription based on
%HRmax. In search of the most effective testing procedures,
research needs to further explore individual heart rate responses to different
exercise protocols.
