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Physical Demands and Power Profile of Different Stage Types within a Cycling Grand Tour

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This study aims to describe the intensity and load demands of different stage types within a cycling Grand Tour. Nine professional cyclists, whom are all part of the same World-Tour professional cycling team, participated in this investigation. Competition data were collected during the 2016 Giro d’Italia. Stages within the Grand Tour were classified into four categories: flat stages (FLAT), semi-mountainous stages (SMT), mountain stages (MT) and individual time trials (TT). Exercise intensity, measured with different heart rate and power output based variables, was highest in the TT compared to other stage types. During TT’s the main proportion of time was spent at the high-intensity zone, whilst the main proportion of time was spent at low intensity for the mass start stage types (FLAT, SMT, MT). Exercise load, quantified using Training Stress Score and Training Impulse, was highest in the mass start stage types with exercise load being highest in MT (329, 359 AU) followed by SMT (280, 311 AU) and FLAT (217, 298 AU). Substantial between-stage type differences were observed in maximal mean power outputs over different durations. FLAT and SMT were characterised by higher short-duration maximal power outputs (5–30 s for FLAT, 30 s–2 min for SMT) whilst TT and MT are characterised by high longer duration maximal power outputs (>10 min). The results of this study contribute to the growing body of evidence on the physical demands of stage types within a cycling Grand Tour.
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European Journal of Sport Science
ISSN: 1746-1391 (Print) 1536-7290 (Online) Journal homepage: http://www.tandfonline.com/loi/tejs20
Physical demands and power profile of different
stage types within a cycling grand tour
Dajo Sanders & Mathieu Heijboer
To cite this article: Dajo Sanders & Mathieu Heijboer (2018): Physical demands and power profile
of different stage types within a cycling grand tour, European Journal of Sport Science
To link to this article: https://doi.org/10.1080/17461391.2018.1554706
Published online: 27 Dec 2018.
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ORIGINAL ARTICLE
Physical demands and power profile of different stage types within a
cycling grand tour
DAJO SANDERS
1,2
& MATHIEU HEIJBOER
3
1
Physiology, Exercise and Nutrition Research Group, University of Stirling, Stirling, UK;
2
Sport, Exercise and Health Research
Centre, Newman University, Birmingham, UK &
3
Team LottoNL-Jumbo Professional Cycling Team, Amsterdam,
Netherlands
Abstract
This study aims to describe the intensity and load demands of different stage types within a cycling Grand Tour. Nine
professional cyclists, whom are all part of the same World-Tour professional cycling team, participated in this
investigation. Competition data were collected during the 2016 Giro dItalia. Stages within the Grand Tour were classified
into four categories: flat stages (FLAT), semi-mountainous stages (SMT), mountain stages (MT) and individual time
trials (TT). Exercise intensity, measured with different heart rate and power output based variables, was highest in the TT
compared to other stage types. During TTs the main proportion of time was spent at the high-intensity zone, whilst the
main proportion of time was spent at low intensity for the mass start stage types (FLAT, SMT, MT). Exercise load,
quantified using Training Stress Score and Training Impulse, was highest in the mass start stage types with exercise load
being highest in MT (329, 359 AU) followed by SMT (280, 311 AU) and FLAT (217, 298 AU). Substantial between-
stage type differences were observed in maximal mean power outputs over different durations. FLAT and SMT were
characterised by higher short-duration maximal power outputs (530 s for FLAT, 30 s2 min for SMT) whilst TT and
MT are characterised by high longer duration maximal power outputs (>10 min). The results of this study contribute to
the growing body of evidence on the physical demands of stage types within a cycling Grand Tour.
Keywords: training, performance, cycling, power output, heart rate
Highlights
.A professional cycling Grand Tour consists of varying stage types such as time trials, flat stages and (semi-)mountain stages.
.Substantial differences in intensity and exercise load demands, quantified using heart rate and power output based metrics,
are observed between different stage types.
.The detailed description of such demands, and the identified differences between different stage types, can inform training
strategies in preparation for these races.
Introduction
Three-week cycling Grand Tours are one of the most
physically demanding events in competitive sport
worldwide. A big proportion of professional road
cyclists will participate in at least one, if not more,
of the three week Grand Tours: the Giro dItalia,
Tour de France and Vuelta a España (Lucia, Hoyos,
& Chicharro, 2001). Over recent years we have
obtained more and more information on the physio-
logical characteristics of professional cyclists specia-
lising in different competition elements that are
present within Grand Tours (Lucia et al., 2001;
Padilla, Mujika, Cuesta, & Goiriena, 1999; Vogt,
Schumacher, Blum, et al., 2007). Furthermore,
case studies are available on the physiological profiles
of a multiple Tour de France winning cyclist (Bell,
Furber, Van Somren, Anton-Solanas, & Swart,
2017) and a multiple top-10 Grand Tour finisher
(Pinot & Grappe, 2015). Descriptive studies evaluat-
ing the exercise intensity and load characteristics of
Grand Tours have shown the extreme physiological
demands of these races (Lucia, Hoyos, Santalla,
Earnest, & Chicharro, 2003; Padilla et al., 2001;
Padilla, Mujika, Orbananos, & Angulo, 2000;
© 2018 European College of Sport Science
Correspondence: Dajo Sanders, Physiology, Exercise and Nutrition Research Group, University of Stirling, FK9 4LA Stirling, UK. Email:
dajosanders@gmail.com
European Journal of Sport Science, 2018
https://doi.org/10.1080/17461391.2018.1554706
Padilla, Mujika, Santisteban, Impellizzeri, & Goir-
iena, 2008). Upon recently, the main variable to
monitor these athletes was heart rate, and intensity
and load were based on data collected using heart
rate monitors. For example, Padilla et al. (2000,
2001,2008), in a series of studies, evaluated the exer-
cise intensity and load during cycling competitions by
describing the time spent in different heart rate zones
and exercise load using the heart rate-based Training
Impulse (TRIMP) metric. The studies described
have shown that quantification of intensity distri-
bution and exercise load, using heart rate-based
metrics, can reflect the physiological demand of
different competition settings in professional
cycling. Even though these studies have also provided
(some) power output demands, power output in
these studies was estimated based on the individuals
heart rate power output relationship from linear
regression equations based on laboratory incremental
test results (Padilla et al., 2001). However, due to
technological advancements over recent years with
mobile power meters, the collection of both physio-
logical (i.e. heart rate) and work rate (i.e. power
output) data in the field is now widely possible to
monitor the training and competition of (elite)
cyclists. Indeed, a number of studies have reported
that power meters can be used as an accurate moni-
toring tool for coaches, athletes or sport scientists
(Bouillod, Pinot, Soto-Romero, Bertucci, &
Grappe, 2017; Gardner et al., 2004).
Since power output is now so widely used in (pro-
fessional) cycling to guide training sessions or
analyse performance, studies are now available that
describe the power output demands of professional
cycling as well, with a particular interest for the
demands of different competition elements
(Abbiss, Menaspa, Villerius, & Martin, 2013;
Menaspa, Quod, Martin, Peiffer, & Abbiss, 2015;
Padilla et al., 1999; Padilla et al., 2000; Padilla
et al., 2001; Padilla et al., 2008; Vogt et al., 2006).
Vogt, Schumacher, Roecker et al. (2007) described
the power output demands of different stages (flat
(FLAT), semi-mountainous (SMT) and mountain
(MT) stages) within the Tour de France in fifteen
professional cyclists. They showed, for example,
that mean power output was highest in MT (234 ±
13 W [3.3 ± 0.2 W/kg]), followed by SMT (228 ±
22 and [3.3 ± 0.3]) and FLAT stages (218 ± 18 W
[3.1 ± 0.3 W/kg]). In addition, studies have quanti-
fied the maximal mean power outputs over different
durations (151800 s) achieved during the varying
stage types (Vogt, Schumacher, Roecker et al.,
2007; Vogt, Schumacher, Blum, et al., 2007). The
quantification of such record power outputs
(Pinot & Grappe, 2011)orPower Profile(Allen
& Coggan, 2010) provides valuable evidence on
the differences in power output demands between
a variety of stage types.
The highlighted previous studies have contributed
to a greater understanding of intensity and load
demands of professional cycling races and potential
differences between a variety of competition
elements. However, previous studies evaluating
intensity and load demands of professional cycling
Grand Tours have been conducted more than 10
years ago, warranting an update. In addition,
besides the study by Vogt, Schumacher, Roecker
et al. (2007) and another case-study describing the
power output produced during flat and mountain
stages in the Giro dItalia (Vogt, Schumacher,
Blum, et al., 2007), additional evidence on the
power output demands of different competition
elements within a Grand Tour is needed. Lastly, the
load demands of competition elements within a
Grand Tour have mainly been quantified using
heart rate based (i.e. TRIMP) measures (Padilla
et al., 2000; Padilla et al., 2001; Padilla et al., 2008),
whilst the description of external load measures (e.g.
Training Stress Score [TSS]) commonly used in
cycling, can provide valuable additional insight.
Accordingly, the purpose of this study was to
expand and update the current evidence base using
an in-depth analysis of the physical demands of a pro-
fessional cycling Grand Tour race and different stage
types within a Grand Tour.
Methods
Participants
Nine professional cyclists, whom are all part of the
same World-Tour professional cycling team, agreed
to participate in this investigation. Participants were
Table 1. Subject characteristics and laboratory measurements
(n = 9) obtained during incremental tests.
Variables Mean ± SD Range
Age (yr) 30 ± 5 2539
Height (cm) 182 ± 6 174193
Body mass (kg) 72.7 ± 5.6 64.582.0
PO at LT
1
(W) 301 ± 33 248360
HR at LT
1
(beats min
1
) 154 ± 12 137168
PO at LT
2
(W) 360 ± 27 332410
HR at LT
2
(beats min
1
) 170 ± 12 151185
W
max
(W) 420 ± 28 385478
W
max
(W kg
1
) 5.8 ± 0.3 5.46.2
HR
max
(beats min
1
) 185 ± 11 168201
VO
2max
(ml min
1
kg
1
)74±6 6685
Abbreviations: PO: power output; LT
1
: first lactate threshold; HR:
heart rate; LT
2
: second lactate threshold; W
max
: maximal power
output achieved during the test; HR
max
: maximal heart rate;
VO
2max
: maximal oxygen uptake.
2D. Sanders & M. Heijboer
informed of the purpose and procedures of the
investigation. Written consent was obtained and
institutional ethics approval was granted and in
agreement with the Helsinki Declaration. Physical
characteristics of the participating cyclists are pre-
sented in Table 1. The participants roles within the
analysed race consisted of one general classification
contender, one sprinter and seven domestiques. In
addition, four of the seven domestiques were
considered good time trialists, having finished in the
top 20 of Grand Tour individual time trials either
within the analysed race or in previous races
Testing
Prior to the study (January), each participant per-
formed a laboratory incremental test. The test
started at a workload of 2.50 W kg
1
and increased
with 0.50 W kg
1
every 3 min until exhaustion.
Each cyclist performed the test on their own
bicycle, which was placed on an ergometer
(Cyclus2 ergometer, RBM Electronics, Leipzig,
Germany). Gas exchange was measured continu-
ously using a breath-by-breath gas analysis system
(Metalyzer 3B, Cortex, Leipzig, Germany) and
lactate measures were taken at the end of every
3 min stage and analysed directly using a portable
lactate analyser (Lactate Pro, Arkray KDK, Japan).
Three individual heart rate and power output zones
were established around a first (LT
1
) and second
lactate threshold (LT
2
) with LT
1
defined at
0.4 mmol L
1
rises above baseline (Bourdon, 2013)
and LT
2
defined using the modified D
max
method
(Bishop, Jenkins, & Mackinnon, 1998). Three
zones were proposed using previously established
protocols (Sanders, Myers, & Akubat, 2017; Seiler
& Kjerland, 2006): zone 1, LT
1
; zone 2, >LT
1
and < LT
2
; zone 3 LT
2
. Peak heart rate obtained
during the incremental test was used as a measure
of maximal heart rate (HR
max
). The last completed
stage was used as the measure of maximal aerobic
power output (W
max
). If the stage was not completed
W
max
was calculated based on the fraction of the
completed stage where volitional exhaustion
occurred (Kuipers, Verstappen, Keizer, Geurten, &
van Kranenburg, 1985). The test was performed
until complete exhaustion to estimate V
̇
O
2max
. After
the test, breath-by-breath values were visually
inspected and V
̇
O
2max
was defined as the highest
30 s mean obtained during the test.
Physical demands and power profile
Competition data was collected during the 2016 Giro
dItalia. Stages were classified into four categories:
flat stages (FLAT), semi-mountainous stages
(SMT), mountain stages (MT) and individual time
trials (TT). Stages were classified based on similar,
albeit slightly adapted, criteria previously described
(Padilla et al., 2001; Vogt, Schumacher, Blum,
et al., 2007): FLAT total distance riding uphill was
shorter than 13 km, uphill sections are scattered
along the stage with a maximum total elevation gain
of 2200 m, but never at the end of it; SMT, with a
total uphill distance of approximately 1335 km,
total elevation gain of minimum 2500 m or less
when the climbs were in the last part of the stage;
MT, in which the total uphill distance was longer
than 35 km with minimum total elevation gain of
3000 m or the stage finished uphill with a climb of
at least 10 km. The first, ninth and fifteenth stage of
the 2016 Giro dItalia included individual TTs.
Elevation gain as well as heart rate and power
output were continuously measured (1 Hz) during
every stage (Pioneer Power Meter, Kawasaki, Kana-
gawa, Japan). Relative power output (W kg
1
) was
calculated using the athletes pre-race bodyweight.
As part of a previous study, the concurrent validity
of the Pioneer power meter was evaluated by compar-
ing it to a Cyclus2 ergometer showing a standard
error of estimate of 6 W (Sanders, Taylor, Myers, &
Akubat, 2017). Riders were instructed to perform
zero-offset procedures prior to each stage according
to manufacturersinstructions. The distribution of
exercise time during the stages was analysed in
relation to the above-mentioned zones around LT
1
and LT
2
. Intensity distribution around these zones
was calculated to provide an descriptive of the inten-
sity characteristics of the different stage types within
the Grand Tour. Also, after every stage, rating of per-
ceived exertion (RPE) was measured using the CR-
10 scale proposed by Borg, Hassmen, and Lager-
strom (1987) based on the question: How hard
was todays stage?
As a measure of external load the Training Stress
Score(TSS) proposed by Coggan (2003) was
measured using the following formula:
TSS =
t×NPTM ×IFTM
FTP ×3600 ×100
where tis the duration of the exercise bout, NPis
normalised power of the exercise bout (Coggan,
2003), IFis intensity factor which is the ratio
between the NP and the individuals FTP (Coggan,
2003). FTP is the individuals functional threshold
power. In this study, FTP was estimated using the
laboratory incremental test data using the modified
D
max
. method (Bishop et al., 1998). As a heart rate-
based internal load measure the TRIMP method
Physical demands and power profile of different stage types within a cycling grand tour 3
proposed by Lucia et al. (2003) was used. TRIMP
was calculated based on the time spent in three pre-
defined heart rate zones. Zones were defined as
zone 1 below LT
1
, zone 2 between LT
1
and LT
2
and zone 3 above LT
2
, a different approach com-
pared to the original Lucias TRIMP that used venti-
latory thresholds to identify the zones (Lucia et al.,
2003). Each zone is given a coefficient of 1, 2 and
3, respectively. Time spent in each zone is multiplied
by the coefficient and then summated to provide a
total TRIMP score (Lucia et al., 2003). Both this par-
ticular TRIMP method and TSS have previously
been shown to have a strong doseresponse relation-
ship with changes in aerobic fitness in competitive
road cyclists (Sanders, Abt, Hesselink, Myers, &
Akubat, 2017).
Statistical analysis
Race characteristics (volume, intensity, load) variables
were compared between stage types using a multilevel
random intercept model using Tukeysmethodfor
pairwise comparisons in R (R: A Language and
environment for statistical computing, Vienna,
Austria). Random effect variability was modelled
using a random intercept for each individual partici-
pant. Level of significance was established at P<.05.
Magnitude-based inferences were used to describe
the magnitude of the differences (Hopkins, Marshall,
Batterham, & Hanin, 2009). Standardised effect size
is reported as Cohensd, using the pooled standard
deviation as the denominator. Qualitative interpret-
ation of dwas based on the guidelines provided by
Hopkins et al. (2009)00.19 trivial; 0.200.59 small;
0.61.19 moderate; 1.201.99 large; 2.00 very large.
Results
A total of 165 stages were analysed (FLAT = 45,
SMT = 44, MT = 55, TT = 21). Race characteristics
are presented in Table 2. Early retirement in the
race due to crashes or illness and/or technical issues
with the mobile power meter resulted in incomplete
datasets for some participants for some stages.
Stages with incomplete datasets were excluded from
the overall analysis. SMT and MT stages were moder-
ate to largely longer in duration (d= 0.781.21) com-
pared to FLAT stages. However, MT was moderate to
largely shorter (d= 0.921.68) in terms of distance
compared to FLAT and SMT.
Intensity and load demands of the different stage
types are presented in Table 2. All the intensity
measures were highest in the TT compared to the
other (mass start) stage types. Mean heart rate and
mean power output were very largely higher (d=
3.545.48) in TT compared to the other stage
types. Peak heart rate was moderately higher in TT
compared to MT and FLAT (d=0.610.63) and
largely higher compared to SMT (d= 1.38). Mean
heart rate and power output was large to very largely
higher during MT compared to FLAT (d=1.68
2.42) and SMT (d= 1.851.87). During TTs, the
main proportion of time was spent at high intensity
(zone 3) for both heart rate (83.3 ± 11.8%) and
power output (64.1 ± 24.6%) (Figure 1). For
FLAT, SMT and MT the main proportion of time
Table 2. Race characteristics, intensity and load demands of the different stage types within the Grand Tour.
FLAT (n= 45) SMT (n= 44) MT (n= 55) TT (n= 21)
Duration (min) 277 ± 21 311 ± 35
a
311 ± 66
a
38 ± 24
a,b,c
Distance (km) 189 ± 15 214 ± 23
a
167 ± 33
a,b
21 ± 15
a,b,c
Speed (km h
1
) 40.5 ± 1.26 40.6 ± 1.9 32.1 ± 3.4
a,b
36.5 ± 12.9
a,b,c
Elevation gain (m) 849 ± 684 1772 ± 784
a
3814 ± 996
a,b
377 ± 388
a,b,c
RPE 5.8 ± 1.9 6.5 ± 1.3 7.8 ± 1.5
a,b
6.8 ± 2.1
a,b
HR (beats min
1
) 125 ± 9 128 ± 4 141 ± 10
a,b
177 ± 10
a,b,c
%HR
max
67 ± 5 67 ± 2 76 ± 5 97 ± 2
HR
peak
(beats·min
1
) 177 ± 10 173 ± 4 177 ± 11 184 ± 12
PO (W) 196 ± 29 217 ± 20
a
254 ± 19
a,b,c
371 ± 47
a,b,c
PO (W kg
1
) 2.68 ± 0.32 2.99 ± 0.27
a
3.50 ± 0.31
a,b
5.14 ± 0.79
a,b,c
TSS km
1
1.14 ± 0.19 1.32 ± 0.20 1.97 ± 0.31
a,b
3.39 ± 1.39
a,b,c
TRIMP km
1
1.55 ± 0.13 1.52 ± 0.14 2.10 ± 0.15
a,b
3.39 ± 0.17
a,b,c
TSS (AU) 217 ± 46 280 ± 40
a
329 ± 83
a,b
62 ± 32
a,b,c
TRIMP (AU) 298 ± 33 311 ± 53 359 ± 80
a
33 ± 32
a,b,c
Abbreviations: RPE: rating of perceived exertion; HR: heart rate; HR
max
: maximal heart rate; HR
peak
: peak heart rate achieved during the
stage; PO: power output; TSS: Training Stress Score; TRIMP: Training Impulse; sRPE: session rating of perceived exertion; FLAT: flat
stage; SMT: semi-mountainous stage; MT: mountains stage; TT: time trial.
a
Significantly different from FLAT.
b
Significantly different compared to SMT.
c
Significantly different compared to MS.
4D. Sanders & M. Heijboer
was spent at low intensity (zone 1) for both heart rate
(93.7 ± 5.4%, 96.2 ± 5.9%, 86.9 ± 13.1%, respect-
ively) and power output (79.5 ± 5.5%, 75.8 ± 5.7%,
68.6 ± 9.3%, respectively). Time spent at moderate
intensity (zone 2) was highest for TT followed by
MT for both heart rate (12.5 ± 5.9 and 11.3 ±
11.0%, respectively) and power output (20.2 ±
21.0% and 12.7 ± 2.9%, respectively).
Load quantified using TRIMP and TSS was very
largely (d= 3.928.15) higher in the mass start stage
types (FLAT, SMT, MT) compared to the individual
TT. Exercise load quantified using TSS and TRIMP
were moderately higher in MT compared to SMT (d
= 0.720.80) and moderate to largely higher compared
to FLAT (d= 1.081.73). Relative load quantified as
TSS km
1
was very largely higher in MT compared
to FLAT and SMT (d= 2.553.32). Similarly,
TRIMP km
1
was also very largely higher in MT
compared to FLAT and SMT (d= 3.934.00).
However, both TSS km
1
and TRIMP km
1
were
highest in TT with the difference being large to very
large (d= 1.6712.3) compared to other stage types.
Figure 1: Proportion of total race time spent at three intensity zones
quantified using heart rate (A) and power output (B) for the differ-
ent stage types. FLAT: flat stage; SMT: semi-mountainous stage;
MT: mountain stage; TT: time trial.
Table 3. Maximal mean power outputs over different durations per stage type.
Stage type 5 s 10 s 30 s 60 s 120 s 5 min 10 min 20 min 30 min
FLAT PO 982 ± 124 836 ± 131 604 ± 121 498 ± 74 436 ± 49 382 ± 39 351 ± 41 322 ± 42 298 ± 44
PO (W kg
1
) 13.42 ± 1.31 11.43 ± 1.50 8.24 ± 1.31 6.81 ± 0.81 5.97 ± 0.56 5.22 ± 0.43 4.80 ± 0.47 4.40 ± 0.50 4.08 ± 0.50
SMT PO 965 ± 121 812 ± 110 605 ± 69 523 ± 49
a
467 ± 33
a
423 ± 29
a
381 ± 28
a
338 ± 27
a
315 ± 30
a
PO (W kg
1
) 13.26 ± 1.45 11.20 ± 1.54 8.35 ± 1.07 7.22 ± 0.75
a
6.44 ± 0.55
a
5.83 ± 0.45
a
5.25 ± 0.47
a
4.67 ± 0.45
a
4.34 ± 0.45
a
MT PO 922 ± 132
a
787 ± 110
a
567 ± 67
a,b
489 ± 58
b
443 ± 31
b
409 ± 27
a,b
391 ± 29
a
374 ± 30
a,b
360 ± 30
a,b
PO (W kg
1
) 12.63 ± 1.54
a
10.78 ± 1.33
a
7.79 ± 0.99
a,b
6.72 ± 0.83
b
6.09 ± 0.56
b
5.62 ± 0.44
a,b
5.37 ± 0.42
a
5.13 ± 0.42
a,b
4.95 ± 0.44
a,b
TT PO 770 ± 135
a,b,c
694 ± 117
a,b,c
572 ± 96 510 ± 52 457 ± 42
a
420 ± 31
a
406 ± 27
a,b,c
375 ± 26
a,b
352 ± 26
a,b
PO (W kg
1
) 10.64 ± 1.72
a,b,c
9.58 ± 1.47
a,b,c
7.92 ± 1.36 7.07 ± 0.81 6.35 ± 0.72
a
5.83 ± 0.51
a
5.62 ± 0.44
a,b,c
5.11 ± 0.40
a,b
4.76 ± 0.43
a,b
Abbreviations: PO: power output; FLAT: flat stage; SMT: semi-mountainous stage; MT: mountains stage; TT: time trial.
a
Significantly different from FLAT
b
Significantly different compared to SMT.
c
Significantly different compared to MS.
Physical demands and power profile of different stage types within a cycling grand tour 5
Maximal mean power outputs for various dur-
ations are presented in Table 3. Short (sprint) peak
power outputs (510 s) were moderate to largely
higher in FLAT and SMT type stages compared to
TT (d= 1.041.63). Differences between stage
types for 30 and 60 s maximal mean power outputs
were trivial to small. Maximal mean power output
over a duration of 2 min was moderately higher in
SMT compared to MT and FLAT (d= 0.750.76).
Maximal mean power outputs during a moderate
duration (510 min) were moderate to largely higher in
SMT, MT and TT compared to FLAT (d= 0.81
1.62). Longer duration maximal mean power
outputs (2030 min) were largely higher for MT
and TT compared to SMT and FLAT (d= 1.26
1.68).
Discussion
The purpose of this study was to describe the inten-
sity and load demands of different stage types
within a professional cycling Grand Tour race, quan-
tified using both heart rate and power output based
metrics. The results of this study show the substantial
differences intensity and load demands as well as the
differences in power profile during different stage
types within a Grand Tour. The reported physical
demands and power profiles of different stage types
within a Grand Tour contribute to the growing
body of evidence on the physical demands of pro-
fessional cycling races. The detailed description of
these demands may allow coaches and practitioners
to design training strategies to optimally prepare for
these demands.
Substantial differences in intensity and load
demands were observed between different stage
types. In line with previous results, the intensity of
TTs is substantially higher compared to mass start
stages (Padilla et al., 2000; Padilla et al., 2001;
Padilla et al., 2008). During the TTs the main pro-
portion of time was spent at the higher intensity
zone (i.e. zone 3) for both heart rate and power
output. In addition, a TT was spent at an average
of 97% of HR
max
compared to 6776% for the
mass start stage types. This is higher compared to
previously reported intensity during professional
time trials varying between 80% and 89% of HR
max
for prologue, short and long TTs (Padilla et al.,
2000). Two out of the three analysed TTs in this
study was relatively short with 9.8 and 10.8 km
respectively, potentially allowing a higher intensity
to be maintained compared to previous research. It
has previously been shown that the lower the distance
of the TT, the higher the % of HR
max
that can be
maintained (Padilla et al., 2000). Furthermore,
tactical strategies (i.e. all-out or conservative
approach) will influence the analysed intensity for
that cyclist (Padilla et al., 2000). However, in line
with previous research, TTs are one of the most
demanding events in professional road cycling in
terms of exercise intensity. This is also shown by
the higher relative loads (i.e. TSS km
1
and TRIMP
km
1
)ofTTs compared to mass start stages.
However, due to the lower exercise duration (and dis-
tance) of TTs, overall exercise load is substantially
lower in TTs, which is in line with previous results
(Padilla et al., 2000; Padilla et al., 2001; Padilla
et al., 2008). Given the substantial difference in the
demand of a TT, it is not surprising that cyclists
adopt specific training strategies to prepare for these
demands. Furthermore, previous research has indi-
cated that anthropometrics and physiological vari-
ables of cyclists specialising in TTs can be different
compared to other cycling specialities (Lucia et al.,
2001; Lucia, Hoyos, & Chicharro, 2000; Mujika &
Padilla, 2001; Padilla et al., 1999).
For the mass start stage types, highest intensity and
total exercise load were found for MT followed by
SMT and FLAT. Even though no difference in dur-
ation was observed between MT and SMT, a moder-
ately higher load was observed during MT. This is
line with previous research showing that exercise
load is highest during MT (Padilla et al., 2001).
Mean power output was also higher during MT
(3.50 W kg
1
), followed by SMT (2.99 W kg
1
) and
FLAT (2.68 W kg
1
). A recent study observed a
mean power output of 3.0 W kg
1
in a large sample
of male professional cycling races (Sanders, van
Erp, & de Koning, 2018). In the current study,
power output during MT was higher than the
observed mean value in Sanders, van Erp, et al.
(2018), SMT was similar and mean power output
during FLAT was lower. Sanders, van Erp, et al.
(2018) didnt differentiate between different type of
races or competition elements, making further com-
parisons not possible. Vogt, Schumacher, Roecker
et al. (2007) showed a relative mean power output
of 3.1 ± 0.3 W kg
1
for FLAT stages, 3.3 ±
0.3 W kg
1
for SMT and 3.3 ± 0.2 W kg
-1
for MT
in the Tour de France. Even though mean power
output was higher for MT, we observed a lower
mean power output during FLAT and SMT stages.
As total elevation gain for each stage type was not
reported, it is hard to directly compare these
results. However, even though some discrepancies
in overall results between studies can be observed,
this study confirms previous studies showing that
total elevation gain is a major contributor to overall
exercise load during cycling Grand Tours with a
greater total elevation resulting in greater exercise
load. In addition, during MT, a greater proportion
6D. Sanders & M. Heijboer
of time is spent at moderate(i.e. zone 2) and
high(i.e. zone 3) intensity, quantified with both
heart rate and power output, compared to FLAT
and MT. This is line with previous research
showing that time spent at an HR at and above the
lactate threshold is higher in MT followed by SMT
and FLAT (Padilla et al., 2001). These differences
in demands should be considered when planning
strategies in preparation for different competition
elements.
Differences were observed in maximal mean power
outputs over different durations comparing the differ-
ent stage types. Short-duration power outputs (5
30 s) were higher in FLAT and SMT compared to
MT and TT. Pacing strategies contribute to the
lower maximal mean power outputs over 530 s in
TT compared to mass start stages as a more continu-
ous pacing strategy, without explosive bursts,is
typically adopted (and more favourable) in TTsof
a duration longer than 10 min (Atkinson, Peacock,
Gibson, & & Tucker, 2007). Furthermore, given
the fact that seven stages with low elevation gain
(i.e. FLAT and some SMT) in the 2016 Giro
dItalia ended in mass sprint finishes, this most
likely contributes to the higher 530 s peak power
outputs observed in FLAT and SMT compared to
MT (Menaspa et al., 2015). These results are in
line with Vogt, Schumacher, Roecker et al. (2007)
who observed higher maximal mean powers over a
duration of 1560 s during FLAT and SMT com-
pared to MT in data from the Tour de France.
However, actual power outputs during the mass
start stages (FLAT, SMT, MT) were lower for
most of the durations compared to Vogt, Schuma-
cher, Roecker et al. (2007). When comparing mass
start stages, highest intermediatedurations (2
5 min) maximal mean power outputs were found
for the SMT, most likely due to the increased
amount of (relatively) shorter hills of a duration of
25 min in SMT stages. This is also in line with pre-
vious research showing the highest maximal mean
power outputs over durations of 35 min in SMT
compared to FLAT and MT (Vogt, Schumacher,
Roecker et al., 2007). We further observed higher
maximal mean power outputs over a longer duration
(i.e. 20 and 30 min) during MT and TT compared to
SMT and FLAT. Vogt, Schumacher, Blum, et al.
(2007) compared FLAT and MT within the Giro
dItalia and also observed higher maximal mean
power outputs over longer durations (5 and 30 min)
in MT compared to FLAT (Vogt, Schumacher,
Blum, et al., 2007). In addition, substantially higher
maximal mean power outputs over 30 min were
observed in the Tour de France for MT compared
to SMT and FLAT stages (Vogt, Schumacher,
Roecker et al., 2007). The results of this study
provide further evidence for the notion that race
profile substantially contribute to the maximal mean
power output characteristics of stages within a
Grand Tour (Lucia et al., 2001; Padilla et al.,
2001). For example, shorter uphill sections (i.e.
SMT) will result in highest maximal mean power
outputs over a moderate duration (25min) whilst
with increasing total elevation gain due to long
uphill sections (i.e. MT stages) the long duration
maximal mean power outputs (i.e. 2030 min)
become increasingly important. This study identifies
power outputs at specific durations that increase in
importance depending on the stage type. This
should be considered valuable information for
coaches/practitioners as a better understanding of
the demands of different competition elements can
directly inform training strategies in preparation for
these races (i.e. training could be prescribed/
adapted to match race demands). For example, opti-
mally preparing for key moments in an MT would
most likely favour training targeted at long duration
efforts (i.e. 2030 min) whilst this differs for SMT
and FLAT stages where key moments in the race
require the ability to produce high power outputs
over shorter durations.
One limitation of this study is that the testing was
conducted at a single Grand Tour with a single
cycling team. Hence, one should be cautious in gen-
eralising these results to a wider population of pro-
fessional cycling. However, most of the results
reported in this study are in line with previous
studies investing the intensity and load demands of
professional cycling races. Therefore, these results
of this study contribute to the growing body of evi-
dence with regards to the intensity and load
demands of different stage types within a Grand
Tour. In addition, FTP is typically defined as the
maximal power output that can be sustained over
4560 min and is typically assessed using field-
based time trials (Coggan, 2003). It must be
acknowledged that a specific field-based assessment
of FTP was not included in this study, but estimated
using the laboratory test, which increases the poten-
tial of measurement errors with regards to TSS
(Sanders, Taylor, et al., 2017). In addition, a limiting
factor of the current study design is that the testing
was conducted before the start of the competitive
season. There might be changes in physiological
capacity between the laboratory testing and the
testing of the race which will influence intensity and
load calculations. Nevertheless, since the margin of
improvement is smaller in these highly trained ath-
letes this is somewhat mitigated.
To summarize, this study provides valuable evi-
dence on the demands and power profile of different
competition elements within Grand Tour. It has been
Physical demands and power profile of different stage types within a cycling grand tour 7
shown that TTs are one of the most demanding
events in professional road cycling in terms of exer-
cise intensity. This is shown by the higher relative
loads and proportion of time spent at high intensity
of TTs compared to mass start stages. However,
due to the lower exercise duration (and distance) of
TTs, overall exercise load is substantially lower in
TTs compared to mass start stages. Comparing
mass-start stages, MT is the most demanding for
both intensity and overall load followed by SMT
and FLAT. In terms of maximal mean power
outputs, FLAT and SMT stage types are character-
ised by higher short-duration maximal power
outputs (530 s for FLAT, 30 s2 min for SMT)
whilst TT and MT are characterised by higher
longer duration maximal power outputs (>10 min).
The results of this study contribute to the growing
body of evidence on the physical demands of different
competition elements within a cycling Grand Tour.
The detailed description of such demands, and the
identified differences between different stage types
or competition elements, may allow coaches and
practitioners to design training strategies to optimally
prepare for these demands.
Acknowledgements
We would like to thank the cyclists for their partici-
pation in this investigation.
Disclosure statement
No potential conflict of interest was reported by the authors.
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Physical demands and power profile of different stage types within a cycling grand tour 9
... This in turn allows the demands of racing to be described (Ebert et al. 2005(Ebert et al. , 2006van Erp, Sanders, and Lamberts 2021;Menaspà et al. 2015;Menaspà, Quod, and Martin 2013;, training/racing performance analysis to be conducted (Leo et al. 2021c;Lucia, Hoyos, and Chicharro 2001;Mujika and Padilla 2001;Pinot and Grappe 2011) and training prescription to be quantified (P. Sanders et al. 2020;Sanders and Heijboer 2019a). ...
... Thus far we have discussed methodological approaches in power profiling, however there are also methodological issues that are pertinent to all approaches. Recorded power output values can be highly influenced by the topography of the event (Padilla et al. , 2008Sanders and Heijboer 2019a), differences between single day and multi day stage racing (van Erp and de Koning 2019; van Erp and Sanders 2020; Lucía et al. 2003) and race category . In professional road cycling race category was found to influence power output: higher power outputs over shorter durations (<2 min) were reported in lower ranked races, and higher power outputs over longer durations (>10 min) were observed in races with higher difficulty. ...
... AP values recorded by professionals (4.0 W•kg -1 ) were higher than those reported by Sanders and Heijboer (2019a) (3.5 W•kg -1 ) and (3.1 W•kg -1 ) during mountainous stages; likewise, they were higher than values for hilly stages reported by Ebert et al.(Ebert et al. 2006) (2.9 W•kg -1 ) in a similarly ranked multi day event and Sanders et al. (2020) (2.9 W•kg -1 ) for multi-stage races. A possible explanation for this may be that values from Sanders and Heijboer (2019a) were recorded in grand tours where the race length combined with the accumulation of race days may have resulted in higher accumulated fatigue and lower power output (Rodríguez-Marroyo et al. 2017). Values reported by were recorded in a lower ranked event. ...
Thesis
Monitoring and evaluating the physiological and performance characteristics of endurance athletes provides relevant information about the long-time athletic development, training process and talent identification. While there is growing evidence for the physiological and performance attributes in junior and professional cyclists, limited information is available about the U23 category. Therefore, the aim of this thesis was to examine the longitudinal physiological and performance characteristics of U23 elite cyclists, with a special focus on the application of the power profile and the power-duration relationship. Study 1 involved a critical evaluation of the current literature on power profiling methodologies and the application of the power-duration relationship. In order to improve the predictive ability of the power profile and the power-duration relationship across exercise intensity domains, it is recommended to ensure a high ecological validity (e.g. rider specialization, race demands) during standardized field testing. For this reason, single effort prediction trials outside the severe exercise intensity domain should be avoided, due to a high measurement bias and a low predictive ability regarding the power-duration relationship. Standardized field testing for power profiling should be conducted at least two times per season to obtain an accurate fingerprint of a cyclist’s performance capacity in the field. In addition, future research is required to better understand the fatigue mechanisms and downward-shift of the power profile and power-duration relationship in the moderate and heavy exercise intensity domains following prior heavy exercise. In Studies 2 and 3 the power profile and power-duration relationship were investigated throughout a competitive season in U23 elite cyclists. Study 2 examined the changes in maximal mean power output (MMP) and derived critical power (CP) and work capacity above critical power (W´) obtained during training and racing. The results revealed that the absolute power profile was not significantly different during a competitive season, except changes in the relative power profile due to a reduction in body mass. Study 3 investigated the differences in the power profile derived from training and racing, the training characteristics across a competitive season, and the relationships between the training characteristics and the power profile in U23 elite cyclists. Higher absolute and relative power profiles were recorded during racing than training. Training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season. Studies 4 and 5 analysed the power profile, workload characteristics and race performance in U23 and professional cyclists during a five-day multi-stage race. Study 4 compared the power profile, internal and external workloads, and racing performance between U23 and professional cyclists and between varying rider types, including allrounders, domestiques and general classification (GC) riders. This study demonstrated that the power profile after 1.000-3.000 kJ of total work could be used to evaluate the readiness of U23 cyclists to move into the professional ranks, as well as differentiate between rider types during racing. Study 5 specifically analysed climbing performance in a professional multistage race, and assessed the influence of climb category, prior workload, and intensity measures on climbing performance in U23 and professional cyclists. The findings indicated that climbing performance in professional road cycling is influenced by climb categorization as well as prior workload and intensity measures. Professional cyclists displayed better climbing performance than U23 cyclists, while the workload and intensity measures were higher in U23 than professional cyclists. Collectively the studies within this thesis have contributed to an improved understanding of the physiological and performance attributes of U23 elite cyclists in their maturation to the professional level. These studies have confirmed the practical application of the power profile and power-duration relationship for performance evaluation and prediction during training and racing. This thesis has enabled detailed insights about factors affecting the power profile and the power-duration relationship, and it has provided a concise applied strategy for the inclusion of power profiling in the longitudinal athletic development pathway to maximize cycling performance.
... Thus, it is still unclear what influence the actual power output in the competition has on the competition result. Although performance in-competition data from professional athletes are increasingly available via platforms such as Strava, the quantity of available data is still insufficient to calculate the effect on the competition result (Sanders and Heijboer, 2019;Van Erp and Sanders, 2020;Westlake, 2020;Van Erp et al., 2021a). ...
... The winning time in the different stages of the men's race ranged between 41 and 59 min. Accordingly, the length of the virtual stages corresponds more to that of a time trial, which in the Virtual Tour de France was 36.8 km long, and the winning time was 55 min (Sanders and Heijboer, 2019;Procyclingstats, 2020). Women raced the same distances as men in the Virtual Tour de France, with winning times ranging from 47 to 66 min. ...
Article
Full-text available
Background: Mixed-reality sports are increasingly reaching the highest level of sport, exemplified by the first Virtual Tour de France, held in 2020. In road races, power output data are only sporadically available, which is why the effect of power output on race results is largely unknown. However, in mixed-reality competitions, measuring and comparing the power output data of all participants is a fundamental prerequisite for evaluating the athlete’s performance. Objective: This study investigates the influence of different power output parameters (absolute and relative peak power output) as well as body mass and height on the results in mixed-reality competitions. Methods: We scrape data from all six stages of the 2020 Virtual Tour de France of women and men and analyze it using regression analysis. Third-order polynomial regressions are performed as a cubic relationship between power output and competition result can be assumed. Results: Across all stages, relative power output over the entire distance explains most of the variance in the results, with maximum explanatory power between 77% and 98% for women and between 84% and 99% for men. Thus, power output is the most powerful predictor of success in mixed-reality sports. However, the identified performance-result gap reveals that other determinants have a subordinate role in success. Body mass and height can explain the results only in a few stages. The explanatory power of the determinants considered depends in particular on the stage profile and the progression of the race. Conclusion: By identifying this performance-result gap that needs to be addressed by considering additional factors like competition strategy or the specific use of equipment, important implications for the future of sports science and mixed-reality sports emerge.
... To the best of the authors' knowledge, only Menaspa et al 5 presented the power profiles of female professional cyclists. The power profile of male cyclists have often been described in literature 3,11,16,17 and is influenced by race type (ie, flat, semimountain, mountain), 13,18,19 race level, 17 and number of race days. 3 Most studies have presented the mean power profile of a group (or team) of cyclists, and as such, the data do not always present the full picture of the race winning effort exerted in a particular race. ...
... 3 Most studies have presented the mean power profile of a group (or team) of cyclists, and as such, the data do not always present the full picture of the race winning effort exerted in a particular race. 3,18 For example, the average 20-minute MMP reported in a mountain stage is 5.1 W·kg −1 ; however, several case studies have clearly indicated that higher values are needed to be successful in a mountain stage. 14,20 However, comparing power profiles of successful (ie, TOP20, TOP10, or TOP5 finishes) with not successful races 5,17,21 can provide valuable information regarding "race winning effort." ...
Article
Introduction: Maximal mean power output (MMP) is commonly used to describe the demands and performances of races in professional male cycling. In the female professional cyclist domain, however, there is limited knowledge regarding MMPs in races. Therefore, this study aimed to describe MMPs in female professional cycling races while investigating differences between TOP5 and NOT-TOP5 races. Methods: Race data (N = 1324) were collected from 14 professional female cyclists between 2013 and 2019. Races were categorized as TOP5 or NOT-TOP5. The MMPs were consequently determined over a range of different time frames (5 s to 60 min). To provide these MMPs with additional context, 2 factors were determined: when these MMPs were attained in a race (based on duration and kilojoules spent [kJspent·kg-1]) and these MMPs relative to the cyclist's season's best MMP (MMP%best). Results: Short-duration power outputs (≤1 min) were higher in TOP5 races compared with NOT-TOP5 races. In addition, the timing (both duration and kJspent·kg-1) of all MMPs was later and after more workload in the race in TOP5 compared with NOT-TOP5 races. In contrast, no difference in MMP%best was noted between TOP5 and NOT-TOP5 races. Conclusions: TOP5 races in female cycling are presented with higher short-duration MMPs (≤1 min) when compared with NOT-TOP5 races, and cyclists were able to reach a higher percentage of their seasonal best MMP when they were able to finish TOP5. In addition, these MMPs are performed later and after more kJspent·kg-1 in TOP5 versus NOT-TOP5 races, which confirms the importance of "fatigue resistance" in professional (female) cycling.
... 9 Such approaches have not only proven successful in differentiating cyclists of varying levels, 10,11 with various degrees of specialization, or rider type (sprinter vs climber), 9 but also to evaluate the demands of cycling races. 8 In addition, MMP data can be used in estimating critical power (CP), with power taken from short (120 s) to longer (900 s) durations being used in the calculation of CP and W′. CP is the highest sustainable rate of oxidative metabolism above which occurs a continuous loss of metabolic (phosphocreatine, pH), and systemic (VO 2 , blood lactate concentration) homeostasis. ...
... That is, in a mass-start race setting, the terrain, format, and tactics of races will directly impact the type of effort made. 7,8 Athletes rarely complete a maximal effort for a fixed amount of time within a race and to infer that both a maximal MMP effort was completed and W′ is as close to being fully depleted is difficult. Although recent studies have determined that CP from race data and formal testing are comparable, 14,15 Leo et al 15 found that relying on training data alone, MMP values and subsequent CP and W′ estimations were not sufficient markers of performance capacity in professional U23 cyclists. ...
Purpose: The purpose of this study was to assess the relationship between typical performance tests among elite and professional cyclists when conducted indoors and outdoors. Methods: Fourteen male cyclists of either UCI Continental or UCI World Tour Q1 level (mean [SD]: age 20.9 [2.8] y, mass 68.13 [7.25] kg) were recruited to participate in 4 test sessions (2 test sessions indoors and 2 test sessions outdoors) within a 14-day period, consisting of maximum mean power testing for durations of 60, 180, 300, and 840 seconds. Results: Across all maximum mean power test durations, the trimmed mean power was higher outdoors compared with indoor testing (P < .05). Critical power was higher outdoors compared with indoors (+19 W, P = .005), while no difference was observed for the work capacity above critical power. Self-selected cadence was 6 revolutions per minute higher indoors versus outdoors for test durations of 60 (P = .038) and 300 seconds (P = .002). Conclusions: These findings suggest that maximal power testing in indoor and outdoor settings cannot be used interchangeably. Furthermore, there was substantial individual variation in the difference between indoor and outdoor maximum mean powers, across all time durations, further highlighting the difficulty of translating results from indoor testing to outdoor, on an individual level in elite populations.
... power output) workloads (van Erp and de Koning 2019;Mujika 2017;Muriel et al. 2021;Padilla et al. 2000;Padilla et al. 2008). This in turn allows the demands of racing to be described (Ebert et al. 2005(Ebert et al. , 2006van Erp et al. 2021b;Menaspà et al. 2015;Menaspà et al. 2013;Vogt et al. 2007b), training/racing performance analysis to be conducted (Leo et al. 2021c;Lucia et al. 2001;Mujika and Padilla 2001;Pinot and Grappe 2011) and training prescription to be quantified (Leo et al. 2020;Sanders et al. 2020;Sanders and Heijboer 2019a). ...
... Thus far we have discussed methodological approaches in power profiling, however, there are also methodological issues that are pertinent to all approaches. Recorded power output values can be highly influenced by the topography of the event (Padilla et al. 2000(Padilla et al. , 2008Sanders and Heijboer 2019a), differences between single day and multi-day stage racing (van Erp and de Koning 2019; van Erp and Sanders 2020; Lucía et al. 2003) and race category (Sanders and van Erp 2021). In professional road cycling race category was found to influence power output: higher power outputs over shorter durations (< 2 min) were reported in lowerranked races, and higher power outputs over longer durations (> 10 min) were observed in races with higher difficulty. ...
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Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P ( t ) = W ′/ t + CP ( W ′—work capacity above CP; t —time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.
... P rofessional road cycling is recognised as one of the most energetically demanding competitive sports. A Grand Tour is composed of 21 stages of almost consecutive daily racing that varies in exercise intensity, duration and terrain (1,2). Within a Grand Tour there are large variations in on-bike exercise energy expenditure (EEE), from ∼1000 kcal during a time trial to>4500 kcal in mountain stages. ...
... Power outputs in professional cycling races have been quantified in numerous studies (2)(3)(4)(5)(6)(7)(8)(9)(10)(11). ...
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Results: Absolute 5MMPfatigue, 12MMPfatigue and relative 12MMPfatigue were significantly lower in late-season compared with early- and mid-season (p < 0.05). The difference in absolute 12MMPfresh and 12MMPfatigue was significantly greater in late than in early- and mid-season.A significant relationship was found between training time below the first ventilatory threshold (Time < VT1) and improvements in absolute and relative 2MMPfatigue (r = 0.43 p = 0.018 and r = 0.376 p = 0.04 respectively); and between a shift towards a polarised training intensity distribution and improvements in absolute and relative 12MMPfatigue (r = 0.414p = 0.023 for both) between subsequent periods. Conclusion: There is greater variability in the fatigue power profile across a competitive season than the fresh power profile.
... 8,9 A number of studies have analyzed the external and internal demands of professional road races, 10,11 comparing men and women events, 12 professional men races with different competitive levels, 13 and altimetric profiles. 14 On the other hand, only one study described the racing demands of youth cycling categories. In that study, however, Rodríguez-Marroyo et al 15 To the best of our knowledge, a cross-sectional analysis of external and internal race demands in JUN, U23, and PRO has not yet been carried out. ...
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Purpose: To compare the race demands of junior (JUN), under 23 (U23), and professional (PRO) road cyclists. Methods: Thirty male cyclists, divided into 3 age-related categories (JUN, n = 10; U23, n = 10; and PRO, n = 10), participated in this study. Race data collected during the 2019 competitive season were retrospectively analyzed for race characteristics, external, and internal competition load. Results: Higher annual and per race duration, distance, elevation gain, Edward's training impulse, total work, and work per hour were observed in PRO versus U23 and JUN, and U23 versus JUN (P < .01). PRO and U23 recorded higher mean maximal power (RPOs) between 5 and 180 minutes compared with JUN (P < .01). Edward's training impulse per hour was higher in JUN than PRO and U23 (P < .01). Accordingly, JUN spent a higher percentage of racing time in high internal intensity zones compared with U23 and PRO, while these 2 categories spent more time at low internal intensity zones (P < .01). Conclusions: JUN races were shorter and included less elevation gain per distance unit compared to U23 and PRO races, but more internally demanding. JUN produced less power output in the moderate-, heavy-, and severe-intensity exercise domains compared with U23 and PRO (RPOs: 5-180 min). U23 and PRO races presented similar work demands per hour and RPOs, but PRO races were longer than U23.
... This is due to several advantages of cycling tourism, namely: (1) cycling tours can be enjoyed by all ages, both children and the elderly, (2) presenting sports activities and tourist activities at the same time, (3) requiring relatively low costs. lighter than other tourism activities, and (4) offering natural panoramas, natural activities of rural community life and natural cultural attractions [9]. ...
... Instantaneous feedback of power output has been shown to improve training intensity adherence by 65% in rowers compared to boat velocity, stroke rate, and coach feedback alone (Lintmeijer et al., 2019). Furthermore, on-water power measurement in rowing has potential widespread value in the quantification of external training load, analysis of race demands, and performance monitoring via power-based benchmarks, all of which have been achieved with instrumentation systems in cycling (Schumacher and Mueller, 2002;Nimmerichter et al., 2011;Sanders and Heijboer, 2019). However, before rowing instrumentation systems can be used with some certainty, their validity must first be established. ...
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Purpose: Instrumentation systems are increasingly used in rowing to measure training intensity and performance but have not been validated for measures of power. In this study, the concurrent validity of Peach PowerLine (six units), Nielsen-Kellerman EmPower (five units), Weba OarPowerMeter (three units), Concept2 model D ergometer (one unit), and a custom-built reference instrumentation system (Reference System; one unit) were investigated. Methods: Eight female and seven male rowers [age, 21 ± 2.5 years; rowing experience, 7.1 ± 2.6 years, mean ± standard deviation (SD)] performed a 30-s maximal test and a 7 × 4-min incremental test once per week for 5 weeks. Power per stroke was extracted concurrently from the Reference System ( via chain force and velocity), the Concept2 itself, Weba (oar shaft-based), and either Peach or EmPower (oarlock-based). Differences from the Reference System in the mean (representing potential error) and the stroke-to-stroke variability (represented by its SD) of power per stroke for each stage and device, and between-unit differences, were estimated using general linear mixed modeling and interpreted using rejection of non-substantial and substantial hypotheses. Results: Potential error in mean power was decisively substantial for all devices (Concept2, –11 to –15%; Peach, −7.9 to −17%; EmPower, −32 to −48%; and Weba, −7.9 to −16%). Between-unit differences (as SD) in mean power lacked statistical precision but were substantial and consistent across stages (Peach, ∼5%; EmPower, ∼7%; and Weba, ∼2%). Most differences from the Reference System in stroke-to-stroke variability of power were possibly or likely trivial or small for Peach (−3.0 to −16%), and likely or decisively substantial for EmPower (9.7–57%), and mostly decisively substantial for Weba (61–139%) and the Concept2 (−28 to 177%). Conclusion: Potential negative error in mean power was evident for all devices and units, particularly EmPower. Stroke-to-stroke variation in power showed a lack of measurement sensitivity (apparent smoothing) that was minor for Peach but larger for the Concept2, whereas EmPower and Weba added random error. Peach is therefore recommended for measurement of mean and stroke power.
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Purpose: This study provides a retrospective analysis of a large competition database describing the intensity and load demands of professional road cycling races, highlighting the differences between men's and women's races. Method: Twenty male and ten female professional cyclists participated in this study. During 4 consecutive years, heart rate (HR), rating of perceived exertion (RPE) and power output (PO) data were collected during both male (n = 3024) and female (n = 667) professional races. Intensity distribution in five HR zones was quantified. Competition load was calculated using different metrics including Training Stress Score (TSS), Training Impulse (TRIMP) and session-RPE (sRPE). Standardized effect size is reported as Cohen's d. Results: Large to very large higher values (d = 1.36 - 2.86) were observed for distance, duration, total work (kJ) and mean PO in men's races. Time spent in high intensity HR zones (i.e. zone 4 and zone 5) was largely higher in women's races (d = 1.38 - 1.55) compared to men's races. Small higher loads were observed in men's races quantified using TSS (d = 0.53) and TRIMP (d = 0.23). However, load metrics expressed per km were large to very largely higher in women's races for TSS∙km-1 (d = 1.50) and TRIMP∙km-1 (d = 2.31). Conclusions: Volume and absolute load are higher in men's races whilst intensity and time spent at high intensity zones is higher in women's races. Coaches and practitioners should consider these differences in demands in the preparation of professional road cyclists.
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This study evaluates the relationship between a field-based 8-min time trial (8MTT) and physiological endurance variables assessed with an incremental laboratory test. Secondly, lactate thresholds assessed in the laboratory were compared to estimated functional threshold power (FTP) from the 8MTT. Nineteen well-trained road cyclists (aged 22 ± 2 yr, height 185.9 ± 4.5 cm, weight 72.8 ± 4.6 kg, VO2max 64 ± 4 ml·min-1·kg-1) participated. Linear regression revealed that mean 8MTT power output (PO) was strongly to very strongly related to PO at 4 mmol∙L-1, PO at initial rise of 1.00 mmol∙L-1, PO at Dmax and modified (mDmax) (r = 0.61 – 0.82). Mean 8MTT PO was largely to very largely different compared to PO at fixed blood lactate concentration (FBLC) of 2 mmol·L-1 (ES = 3.20) and 4 mmol·L-1 (ES = 1.90), PO at initial rise 1.00 mmol∙L-1 (ES = 2.33), PO at Dmax (ES = 3.47) and mDmax (ES = 1.79) but only trivially different from maximal power output (Wmax) (ES = 0.09). The 8MTT based estimated FTP was moderate to very largely different compared to PO at initial rise of 1 mmol∙L-1 (ES = 1.37), PO at Dmax (ES = 2.42), PO at mDmax (ES = 0.77) and PO at 4 mmol∙L-1(ES = 0.83). Therefore, even though the 8MTT can be valuable as a performance test in cycling shown through its relationships with predictors of endurance performance, coaches should be cautious when using FTP and PO at laboratory-based thresholds interchangeably to inform training prescription.
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Purpose: This study aims to evaluate training intensity distribution using different intensity measures based on session rating of perceived exertion (sRPE), heart rate (HR) and power output (PO) in well-trained cyclists. Methods: Fifteen road cyclists participated in the study. Training data was collected during a 10-week training period. Training intensity distribution was quantified using HR, PO and sRPE categorized in a 3-zone training intensity model. Three zones for HR and PO were based around a first and second lactate threshold. The three sRPE zones were defined using a 10-point scale: zone 1, sRPE scores 1-4; zone 2, sRPE scores 5-6; zone 3, sRPE scores 7-10. Results: Training intensity distribution as percentages of time spent in zone 1, zone 2 and zone 3 was moderate to very largely different for sRPE (44.9%, 29.9%, 25.2%) compared to HR (86.8%, 8.8%, 4.4%) and PO (79.5%, 9.0%, 11.5%). Time in zone 1 quantified using sRPE was large to very largely lower for sRPE compared to PO (P < 0.001) and HR (P < 0.001). Time in zone 2 and zone 3 was moderate to very largely higher when quantified using sRPE compared to intensity quantified using HR (P < 0.001) and PO (P < 0.001). Conclusions: Training intensity distribution quantified using sRPE demonstrates moderate to very large differences compared to intensity distributions quantified based on HR and PO. The choice of intensity measure impacts on the intensity distribution and has implications for training load quantification, training prescription and the evaluation of training characteristics.
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Purpose: The aim of this study was to assess the dose-response relationships between different training load methods and aerobic fitness and performance in competitive road cyclists. Method: Training data from 15 well-trained competitive cyclists were collected during a 10-week (December - March) pre-season training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister's TRIMP (bTRIMP), Edwards' TRIMP (eTRIMP), individualized TRIMP (iTRIMP), Lucia's TRIMP (luTRIMP) and session-RPE (sRPE). External load was measured using Training Stress Score™ (TSS). Results: Large to very large relationships (r = 0.54-0.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol·L(-1)) were observed for all training load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = 0.81 [95% CI: 0.51 to 0.93, r = 0.77 [95% CI 0.43 to 0.92]) and TSS (r = 0.75 [95% CI 0.31 to 0.93], r = 0.79 [95% CI: 0.40 to 0.94]). The highest dose-response relationships with changes in the 8MT performance test were observed for iTRIMP (r = 0.63 [95% CI 0.17 to 0.86]) and luTRIMP (r = 0.70 [95% CI: 0.29 to 0.89). Conclusions: The results show that training load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.
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Purpose: This study aimed to determine the validity, sensitivity, reproducibility and robustness of the Powertap (PWT), Stages (STG) and Garmin Vector (VCT) power meters in comparison with the SRM device. Methods: A national-level male competitive cyclist was required to complete three laboratory cycling tests that included a sub-maximal incremental test, a sub-maximal 30-min continuous test and a sprint test. Two additional tests were performed: the first on vibration exposures in the laboratory and the second in the field. Results: The VCT provided a significantly lower 5 s power output (PO) during the sprint test with a low gear ratio compared with the POSRM (-36.9%). The POSTG was significantly lower than the POSRM within the heavy exercise intensity zone (zone 2, -5.1%) and the low part of the severe intensity zone (zone 3, -4.9%). The POVCT was significantly lower than the POSRM only within zone 2 (-4.5%). The POSTG was significantly lower in standing position than in the seated position (-4.4%). The reproducibility of the PWT, STG and VCT was similar to that of the SRM system. The POSTG and POVCT were significantly decreased from a vibration frequency of 48 Hz and 52 Hz, respectively. Conclusions: The PWT, STG and the VCT systems appear to be reproducible, but the validity, sensitivity and robustness of the STG and VCT systems should be treated with some caution according to the conditions of measurement.
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Introduction: This case study reports a range of physiological characteristics in a two-time Tour de France champion. Methods: After body composition assessment (dual-energy x-ray absorptiometry), two submaximal cycling step tests were performed in ambient (20°C, 40%) and hot and humid (30°C, 60% [HH]) conditions from which measures of gross efficiency (GE), lactate-power landmarks, and heart rate responses were calculated. In addition, thermoregulatory and sweat responses were collected throughout. V˙O2peak and peak power output (PPO) were also identified after a separate ramp test to exhaustion. Results: V˙O2peak and PPO were 5.91 L·min (84 mL·kg·min) and 525 W, respectively, whereas mean GE values were 23.0% and 23.6% for ambient and HH conditions, respectively. In addition to superior GE, power output at 4 mmol·L lactate was higher in HH versus ambient conditions (429.6 vs 419.0 W) supporting anecdotal reports from the participant of good performance in the heat. Peak core and skin temperature, sweat rate, and electrolyte content were higher in HH conditions. Body fat percentage was 9.5%, whereas total fat mass, lean mass, and bone mineral content were 6.7, 61.5, and 2.8 kg, respectively. Conclusion: The aerobic physiology and PPO values indentified are among the highest reported for professional road cyclists. Notably, the participant displayed both a high V˙O2peak and GE, which is uncommon among elite cyclists and may be a contributing factor to their success in elite cycling. In addition, performance in HH conditions was strong, suggesting effective thermoregulatory physiology. In summary, this is the first study to report physiological characteristics of a multiple Tour de France champion in close to peak condition and suggests what may be the prerequisite physiological and thermoregulatory capacities for success at this level.
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The aim of this study was to quantify the demands of road competitions ending with sprints in male professional cycling. 17 races finished with top-5 results from 6 male road professional cyclists (age, 27.0±3.8 years; height, 1.76±0.03 m; weight, 71.7±1.1 kg) were analysed. SRM power meters were used to monitor power output, cadence and speed. Data were averaged over the entire race, different durations prior to the sprint (60, 10, 5 and 1 min) and during the actual sprint. Variations in power during the final 10 min of the race were quantified using exposure variation analysis. This observational study was conducted in the field to maximize the ecological validity of the results. Power, cadence and speed were statistically different between various phases of the race (p<0.001), increasing from 316±43 W, 95±4 rpm and 50.5±3.3 km·h(-1) in the last 10 min, to 487±58 W, 102±6 rpm and 55.4±4.7 km·h(-1) in the last min prior to the sprint. Peak power during the sprint was 17.4±1.7 W·kg(-1). Exposure variation analysis revealed a significantly greater number of short-duration high-intensity efforts in the final 5 min of the race, compared with the penultimate 5 min (p=0.010). These findings quantify the power output requirements associated with high-level sprinting in men's professional road cycling and highlight the need for both aerobic and anaerobic fitness. © Georg Thieme Verlag KG Stuttgart · New York.
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Abstract This study analysed the evolution of the physical potential of a twice top-10 Grand Tour cycling finisher (Tour de France and Vuelta a España) whose training was monitored between the ages of 18 and 23 years. The world-class cyclist's power output (PO) data and training indices were analysed over six years to determine the evolution of his record power profile and training load (TL), which were estimated by using the session rating of perceived exertion (RPE) method. The total annual duration and TL increased through six seasons by 79% and 83%, respectively. The record POs in all exercise intensity zones improved over the six years. The increases in TL, monotony (+34%) and strain (+162%) from the junior category to the world-class level significantly correlated with an improvement in his aerobic potential, which was characterised by an increase in the record POs between 5 min and 4 h. This case study of the performance level and training parameters of a world-class cyclist provides comprehensive insight into the evolution of a cyclist to the top level. Furthermore, determining the record power profile of this athlete over six competitive seasons illuminates the maturation of the physical potential of a top-10 Grand Tour finisher.
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A number of laboratory-based performance tests have been designed to mimic the dynamic and stochastic nature of road cycling. However, the distribution of power output and thus physical demands of high-intensity surges performed to establish a breakaway during actual competitive road cycling are unclear. Review of data from professional road-cycling events has indicated that numerous short-duration (5-15 s), high-intensity (~9.5-14 W/kg) surges are typically observed in the 5-10 min before athletes' establishing a breakaway (ie, riding away from a group of cyclists). After this initial high-intensity effort, power output declined but remained high (~450-500 W) for a further 30 s to 5 min, depending on race dynamics (ie, the response of the chase group). Due to the significant influence competitors have on pacing strategies, it is difficult for laboratory-based performance tests to precisely replicate this aspect of mass-start competitive road cycling. Further research examining the distribution of power output during competitive road racing is needed to refine laboratory-based simulated stochastic performance trials and better understand the factors important to the success of a breakaway.