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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.
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European Journal of Applied Physiology (2022) 122:301–316
Power profiling andthepower‑duration relationship incycling:
anarrative review
PeterLeo1 · JamesSpragg2· TimPodlogar3,4· JustinS.Lawley1· IñigoMujika5,6
Received: 2 June 2021 / Accepted: 14 October 2021 / Published online: 27 October 2021
© The Author(s) 2021
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 individual-
ize 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 guide-
lines 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.
Keywords Racing· Training· Analysis· Performance· Prediction· Power output
O2max Fractional utilization of the maximum oxygen
2-P CP Two-parameter critical power model
3-P CP Three-parameter critical power model
APR Anaerobic power reserve
ATP Adenosine tri phosphate
BMX Bicycle motocross
CT Critical torque
CP Critical power
CPTTF Time to task failure at critical power
e Basis of the natural logarithm (e = 2.178)
EVA Exposure variation analysis
FPCA Functional principal component analysis
FTP Functional threshold power
GET Gas exchange threshold
GXT Laboratory incremental graded exercise test
k The rate of the exponential decline in power
LT Lactate threshold
MAP Maximum aerobic power
Communicated by Michael Lindinger.
* Peter Leo
1 Division ofPerformance Physiology & Prevention,
Department ofSport Science, University Innsbruck,
Innsbruck, Austria
2 Health Physical Activity Lifestyle Sport Research
Centre (HPALS), University ofCape Town, CapeTown,
3 Faculty ofHealth Sciences, University ofPrimorska, Izola,
4 Department ofAutomatics, Biocybernetics andRobotics,
Jožef Stefan Institute, Ljubljana, Slovenia
5 Department ofPhysiology, Faculty ofMedicine andNursing,
University oftheBasque Country, Leioa, BasqueCountry,
6 Exercise Science Laboratory, School ofKinesiology, Faculty
ofMedicine, Universidad Finis Terrae, Santiago, Chile
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302 European Journal of Applied Physiology (2022) 122:301–316
1 3
MLSS Maximum lactate steady state
MMP Maximal mean power output
OmPD Omni power duration model
P&T Peronnet and Thibault model
P(t) Power output
Pmax Peak power over 1s
SEE Standard error of the estimate
TT Cycling time trial
TTF Time to task failure
O2 Oxygen uptake
O2max Maximum oxygen uptake
W Work capacity above critical power
WEP Work above end test power
Since the invention of the first mobile power meter for
cycling in the late 1980s training and racing with this tool
has become standard practice in multiple cycling disciplines
including road, track, mountain bike, cyclo-cross, bicycle
motocross (BMX) and triathlon. Mechanical power output
measured by strain gauges, most commonly mounted in the
bike’s crank spindle, crank arm or pedal spindle and con-
nected to a head unit mounted in the handlebar allows power
output data to be accurately recorded in field conditions in
real time (Maier etal. 2017). This enables an in-depth analy-
sis of a cyclist’s mechanical power output during training
and/or competition, and the assessment of an athlete’s endur-
ance capacity outside of a laboratory setting (Passfield etal.
These aforementioned technological innovations have
allowed applied scientific research to be undertaken in
cycling, including real-time measurements of internal (e.g.
heart rate) and external (e.g. 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,
2006; van 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 train-
ing prescription to be quantified (Leo etal. 2020; Sanders
etal. 2020; Sanders and Heijboer 2019a).
Power profiling in cycling is most commonly defined as
the assessment of field derived power outputs, i.e. values
obtained during training and racing (Coggan 2003; Leo etal.
2020). Power profiling can be used for the tracking of longi-
tudinal changes in performance and race analysis (Leo etal.
2021b). There is a growing interest in the theoretical and
practical implications of power profiling. However, to date,
there is no consensus on what constitutes the best practice
for power profiling, especially given that there are numerous
methodological issues and approaches. Therefore, the aim
of this narrative review is to present and discuss existing
practices and methods, their implementation, interpreta-
tion, and practical applications, provide recommendations
to unify power profiling approaches for both practice and
research, and suggest future directions for research.
Measuring power output
Before analysing power output data, it is important to under-
stand how power output is measured during cycling and any
associated methodological issues. In cycling, when a force
is created by the muscles and applied perpendicular to the
bicycle crank arm, one crank arm revolution creates two
angular impulses (one per leg); this results in forward drive.
Optimal force production, and as a result optimal forward
drive, is a complex interplay of innervation, muscle recruit-
ment patterns, the contractile function of muscle as well
as an elastic tendon–muscle interaction and metabolic pro-
cesses occurring in these tissues. The properties of force
generation are often described using physics expressions
such as mean torque or mean power output; the former
describing the force and the latter the amount of work pro-
duced in a given time (Winter etal. 2016). Power output is
often expressed as a steady-state value (e.g. 100W), but this
value is a product of many impulses over a given period of
time or a given proportion of the pedal stroke. Some have
argued that ‘mean power output’ is therefore a more accurate
descriptor (Winter etal. 2016). Notwithstanding the validity
of this argument, for the purposes of this review the authors
will employ the customarily used term ‘power output
throughout. However, it should be noted that power output
does not include the energy used to accelerate the cyclist’s
limbs nor forces applied in non-propulsive directions.
Mechanical (or external) power output can either be meas-
ured by strain gauges or calculated mathematically (Maier
etal. 2017; Martin etal. 1998). Depending on the position of
the strain gauge (e.g., pedal spindle, crank, bottom bracket),
the recorded power output is expected to deviate slightly as
some energy is lost via drivetrain inefficiencies (Coyle etal.
1991; Maier etal. 2017; Martin etal. 1998). This highlights
that power output values derived from different strain gauge
positions may not be comparable. Likewise, different power
meter brands and models have different levels of trueness
and precision. Maier etal (2017) found that while on average
commercially available power meters record at a trueness of
0.9 ± 3.2% some units will deviate by more than 5%. The
authors also reported that some power meter brands have
significantly greater precision than others.
On average Maier etal. (2017) found that the small-
est worthwhile change for the accuracy of commercially
available power meters was 1.1–2.8%. This implies that
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303European Journal of Applied Physiology (2022) 122:301–316
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any performance improvements of less than 1.1% cannot
be accurately quantified by commercially available power
meter devices. However, this value may differ from brand to
brand and model to model. Validation studies have been con-
ducted for most commercially available power meters, but
there is no agreed-upon gold standard to which power meters
should be compared. Therefore, researchers and practitioners
should take note of the comparative measure when assess-
ing the validity of any power output measuring device. We
draw the reader’s attention to the aforementioned study by
Maier and colleagues (2017) for a broader discussion of the
methodological issues surrounding power meter validation.
To ensure high data quality the authors strongly recommend
accurate calibration according to the manufacturer’s recom-
mendations prior to the collection of any power meter data.
Additionally, dynamic (Gardner etal. 2004), static (Wooles
etal. 2005), and day-to-day calibration, known as ‘zero-
offsetting’ are all recommended before data derived from
power meters are used for power profiling purposes.
Methodological approaches topower
Numerous methodologies have been applied in the field
of power profiling. The most basic of these is simply the
reporting of average power output values for a given race or
event (Ebert etal. 2005; Vogt etal. 2007a, b). While this is
the starting point in understanding the demands of a given
event, it fails to fully utilise the full potential of power pro-
filing. Another disadvantage is that unless data are derived
from cyclists with differing performance levels within an
event, this approach does not provide any information on the
demands of peak performance, instead it merely describes
the demands of participation.
A more advanced approach is to describe the power out-
put by time at a given intensity. This approach is normally
described as ‘binning’. Binning is where each power output
value is categorized into a bin; each bin represents a range
of intensities (for example 100–200W). The resulting cat-
egorization of each output value can then be expressed as
either total cumulative time in each bin or as a percentage of
total time. (Abbiss etal. 2010; Ebert etal. 2006; Leo etal.
2021b; Metcalfe etal. 2017). Typically, but not always, the
bins are defined by normalizing the power output to body
mass (for example 4–5W kg−1). However, the suitability of
this approach can be questioned; for example, in some events
aerodynamic drag is a far more important factor than body
mass (Pringle etal. 2011). Besides scaling power output
relative to the frontal area (Padilla etal. 1999), to the best
of the authors’ knowledge no studies have been published
where the bins represent ranges of power output values nor-
malised to aerodynamic drag (W CdA−1).
Binning has advantages in that it can describe the range
of intensities that are required to compete or perform in a
given event. Typically, cycling events are not completed
at a fixed power output; instead, power output is stochas-
tic in nature, even in individual time trials (Gordon 2005).
Whilst binning allows the total time at different intensities
to be described, there are weaknesses with this approach.
Firstly, the choice of the range of intensities for a given bin
will influence the results. Often arbitrary bins are chosen,
based on a given power output normalized to body mass,
for example 5.9–7.9W kg−1. If the range of intensities is
too wide the granularity of the power output data cannot be
captured. Another problem is that binning gives no insight
into the length of individual efforts. The cumulative time
in each power output bin may represent one long effort or
multiple short efforts. Finally, if arbitrary bins are used then
it may be that the range of intensities covered by a single
bin includes power outputs that are both sustainable and
unsustainable from a physiological point of view. A solu-
tion to this problem is to use physiological thresholds to
define the bins (Abbiss etal. 2010; Passfield etal. 2013).
For example, the submaximal physiological thresholds that
define the exercise intensity domains could be determined
during laboratory testing and used to define the bins. While
this approach does give a greater insight into the physiology
of a given event for individual athletes, problems occur when
data from multiple athletes are amalgamated, as the bins,
while representing consistent physiological responses, do
not necessarily represent the same absolute or relative power
output for all athletes.
As previously mentioned, one of the main problems with
binning is that duration of individual efforts are not rep-
resented within the data. However, there is a small body
of work that uses exposure variation analysis (EVA) to try
and overcome this limitation (Abbiss etal. 2010; Passfield
etal. 2013). This approach uses a two-bin system; one set of
bins is used in the traditional manner to describe the inten-
sity. Bins can be associated with either arbitrary values or
physiological thresholds. The second set of bins is used to
describe the duration of individual efforts. Here arbitrary
durations are used, for example 0–5s, 5–10s or > 1min.
The intensity bins are plotted on the x-axis, the duration of
individual efforts is plotted on the z-axis and the percentage
of total race time is plotted on the y-axis (see sample data
in Fig.1).
Whilst the exact power output of individual efforts is
still not displayed, EVA is a very powerful tool to show the
pacing strategy and stochastic nature of power output in a
given event. This approach may be especially powerful to
describe events where lots of short submaximal sprints are
interspersed by periods of recovery, for example cyclo-cross
or Olympic cross country mountain biking. EVA is an effec-
tive way to describe the duration of efforts and recovery
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304 European Journal of Applied Physiology (2022) 122:301–316
1 3
bouts. This information can be valuable for coaches and
practitioners when prescribing interval training sessions to
replicate the demands of an event.
A major limitation of the approaches discussed thus far is
that they fail to describe power outputs for individual efforts.
To do this the mean maximal power output (MMP) approach
can be used (van Erp and Sanders 2020; Puchowicz etal.
2020; Quod etal. 2010; Vogt etal. 2007b). MMP values
represent the highest average power that was recorded for a
given (arbitrary) duration, during an event. For example, the
highest average power output recorded over a 5min duration
in a race would be the 5min MMP. Such MMP data are very
valuable because they make it possible to identify the power
output and duration that a cyclist is required to produce to
be competitive in an event. For example, MMP data analysis
shows that a top male general classification contender in a
grand tour is required to produce 5.8W kg−1 for 20min on
key mountain climbs (van Erp etal. 2020a, b). For coaches
and practitioners this is very valuable information.
There are however some fundamental issues with MMP
data. Firstly, it is not known if the recorded MMP values
were derived from a maximal effort. This contrasts with
values derived from formal testing where the maximality
of an effort can be verified. For example, in a traditional
laboratory incremental graded exercise test (GXT) a given
perception of effort and respiratory exchange ratio need to be
obtained for the test to be considered maximal in nature and
therefore a valid maximum oxygen uptake (
O2max) value to
be obtained (Jones etal. 2016). It is hypothesised that almost
none of the MMP values derived from races are maximal
in nature. If a rider were to produce a maximal effort at
any point other than at the finish of a race, it may compro-
mise their ability to subsequently follow their competitors
in bunch events or compromise their pacing strategy in indi-
vidual events (Leo etal. 2021b, c). Secondly, MMP data
from a specific (arbitrary) duration could be the result of
the bracketing of a subsection of a longer effort, or a shorter
duration effort and a subsequent recovery (Leo etal. 2021a,
b). For example, it is very unlikely that a 5-min MMP value
derived from a race represents a maximal effort of exactly
5min in duration. As a result, there is a high probability of
an inherent underestimation of maximal power output when
using MMP values alone. MMP data are only indicative of
what a cyclist did, not what the cyclist is capable of.
Another issue with MMP data in research is that there is
no agreed-upon set of (arbitrary) durations that are being
applied. This means that when trying to compare data from
various studies coaches and practitioners cannot perform
like for like comparisons. This situation has improved some-
what as research groups have started to incorporate a wider
range of MMP durations from ~ 5 to ~ 1800s. This allows a
power-duration curve to be developed using the MMP val-
ues, allowing for some comparisons between studies. A final
issue with MMP analysis is that it may not actually define
‘race winning efforts’. Recent work by Leo and colleagues
(2021b) and van Erp and colleagues (2021a; b) showed that
the power output that cyclists produce falls throughout an
event; and that MMP values are not predictive of race per-
formance. Instead, it is the power output that riders produce
at key moments in the race that is predictive of performance.
For example, in the case of a sprinter in road cycling it is
the power that they can produce in the final moments of the
race that is important, but this is not necessarily the same
as their 10s MMP. This means that MMP analysis may
be missing the very efforts that it is trying to identify. To
better identify these race-winning efforts an approach has
been taken in research whereby the event is broken down
into segments and MMP values in each segment have been
reported (van Erp etal. 2021a; Leo etal. 2021b; Sanders and
van Erp 2021). To date, these segments have been defined
Fig. 1 EVA—exposure varia-
tion analysis in the final hour of
a race in six U23 cyclists (N = 6)
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305European Journal of Applied Physiology (2022) 122:301–316
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via accumulated work, either absolute values or normal-
ized to body mass, for example MMP values after 2.500kJ
of work. However, this approach, which has thus far only
been applied in road cycling has introduced some further
limitations. Road cycling is a team sport in which riders
perform individual tasks such as sheltering a team leader or
collecting nutrition from a following car. It is not the goal of
every rider to try and win the race. Therefore, the reported
decrease in MMP values, as accumulated work increases,
may partially be a product of the fact that some riders have
simply finished their tasks and are therefore no longer pro-
ducing maximal efforts.
To alleviate the problem of arbitrary MMP durations not
matching actual effort durations, some studies have selected
specific sections of the event and identified power output
exclusively in that section (Jobson etal. 2008; Leo etal.
2021c; Padilla etal. 2008). For example, Leo and colleagues
(2021a; b) looked at MMP values exclusively on classified
climbs. This approach, while potentially beneficial in certain
circumstances, does require researchers to identify the key
moments in races for analysis. While this may be possible
for some events, such as a road race stage that starts out flat
and concludes with a mountain top finish, it is not always
possible to accurately identify the key moment in a race. A
possible solution to this is to seek the input of athletes when
identifying the key periods in the race. Whilst an attrac-
tive proposition, to the best of the authors’ knowledge this
approach has not been used in published research.
As mentioned before, the major issue with MMP analysis
is the uncertainty surrounding whether an effort was maxi-
mal in nature, and whether the MMP duration is equal to the
effort duration. To counter this problem, the authors recom-
mend using power output values derived from formal test-
ing to provide a comparative measure to MMP values. This
approach has particular benefits for coaches and practition-
ers as comparisons between MMP data and formal testing
data can be used to monitor changes in the power profile;
and if a rider records a MMP value which exceeds the pre-
diction from formal testing a new formal performance test
can be scheduled. This is particularly useful when analysing
performance in timed events where the in-competition power
output and event duration can be compared to the theoreti-
cal power-duration relationship. This example highlights
the importance of developing a power-duration relationship
rather than simply using standard duration performance
tests, as the likelihood of the test and competition durations
being identical is low. For methodological issues surround-
ing the development of theoretical power duration relation-
ships please see the section ‘Deriving a power-duration
relationship’ below.
Unfortunately, this approach (i.e. comparing MMP
against a pre-established theoretical power-duration relation-
ship derived from prior formal testing) was only undertaken
by a few research groups (Leo etal. 2020; Leo etal. 2021b;
Nimmerichter etal. 2020; Quod etal. 2010). However, all
research has shown good to very good agreement between
power output values from formal testing and MMP values.
Of particular interest is work by Leo and colleagues (2020,
2021a) that shows the formal testing values are only predic-
tive of race performance for a 6-month period before formal
re-testing is required.
Methodological issues
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, 2008; Sanders 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 lower-
ranked races, and higher power outputs over longer durations
(> 10min) were observed in races with higher difficulty.
Another important consideration when performing power
profiling are environmental factors. Altitude, temperature,
and humidity can all influence the power output athletes can
produce. Therefore, from a research perspective the authors
recommend that the environment and race conditions should
be reported whenever possible.
Recent research has also shown that power profiling anal-
ysis conducted exclusively on either training or racing data
produces different results in the same participants (Leo etal.
2020). This is an important factor and further highlights
the need to provide adequate information on the context in
which any power profiling data were collected.
Finally, in competition settings, alongside the aforemen-
tioned issues surrounding team roles there is an influence of
other team-mates and competitors on power output due to
drafting, which lowers the power output requirement for a
given speed (Ouvrard etal. 2018, van Druenen and Blocken
2021). Research has also suggested that competition may
influence the pacing strategy adopted by cyclists (Bossi etal.
Deriving apower‑duration relationship
When power output is plotted against time to task failure
(TTF) a consistent power-duration relationship emerges
(Burnley and Jones 2018). The first researchers to math-
ematically describe this relationship were Monod and
Scherrer (1965) who analysed muscle fatigue during static
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306 European Journal of Applied Physiology (2022) 122:301–316
1 3
and dynamic work (knee extension exercise) and created a
mathematical model describing the hyperbolic relationship
between completed work and TTF. Due to the strong scien-
tific evidence over decades (Burnley and Jones 2018; Jones
etal. 2010; Poole etal. 2016) the power-duration relation-
ship can be considered to represent an integrative approach
to the limits of tolerable exercise in humans.
From a physiological perspective the power-duration
relationship is comprised of four distinct exercise intensity
domains; namely, moderate, heavy, severe, and extreme
(Burnley and Jones 2007), which are characterised by dis-
tinct whole-body physiological responses (Jamnick etal.
2020; Vanhatalo etal. 2016; Whipp 1996). While a complete
physiological background on the systemic and mechanistic
bases of the power-duration relationship would be beyond
the scope of this narrative review, interested readers are
referred to the following review articles: Burnley and Jones
(2018), Jones etal. (2010), Poole etal. (2016), Poole etal.
(2021), Vanhatalo etal. (2016).
Various models are available to coaches and practitioners
to model the power-duration relationship for use in power
profiling (Sreedhara etal. 2019). However, most models only
cover a specific section of the power-duration relationship
(see Fig.2).
Modelling power output intheextreme exercise
intensity domain
Previous research (Bundle etal. 2003; Bundle and Wey-
and 2012; Weyand etal. 2006) has demonstrated that the
anaerobic power reserve (APR) is capable of predicting
short duration (< 3min) power outputs within the extreme
exercise intensity domain, where
O2max may not be attained
before task failure occurs. The APR approach was initially
developed in laboratory settings where the maximum aero-
bic power (MAP) recorded during a GXT and the maximal
power an athlete can produce over one pedal revolution or
over one second (Pmax) are used as parameter inputs. How-
ever, Sanders etal. (2017, 2019b) developed a field testing
method where 3min MMP can be used as a surrogate for
MAP. In this approach the time constant (k), which can be
defined as the rate of the exponential decline in power out-
put (i.e. the reciprocal of the corresponding time constant:
k = 1/τ), can be varied between values of 0.024–0.027 to best
Fig. 2 An illustration of the spectrum of physiological responses
across the power-duration relationship using arbitrary power output
values. Pmax 1s peak power, W work above critical power, CP criti-
cal power, LT lactate threshold, GET gas exchange threshold, APR
anaerobic power reserve model, 2-P CP two-parameter critical power
model, 3-P CP three-parameter critical power model, P&T Peronnet
and Thibault Model, OmPD omni power duration model
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307European Journal of Applied Physiology (2022) 122:301–316
1 3
fit the MMP data. This allows for an individualisation of the
power-duration relationship modelling, which may provide
a better fit (Sanders and Heijboer 2019b) [see sample data
in Fig.3 and Table1 (Eq.1)].
Alongside the APR model, power output in the extreme
exercise intensity domain can also be predicted using the
three-parameter critical power (3-P CP) (Morton 1996), the
Peronnet and Thibault model (P&T) (1989) and Puchow-
icz’s omni power duration model (OmPD) (Puchowicz etal.
2020). It should be noted that in the P&T model, Pmax is pro-
vided as a parameter estimate, whereas in the APR model,
3-P CP model and the OmPD model Pmax is required as an
input parameter. These different modelling approaches con-
siderably influence power output predictions in the extreme
exercise intensity domain (see Fig.4).
Modelling power output inthesevere exercise
intensity domain
Multiple approaches based on the CP concept have been
proposed to predict power outputs within the severe exercise
intensity domain. Although all CP models are equivalent
from a mathematical perspective (i.e. they can be derived
mathematically from one another) they produce different sta-
tistical parameter estimates for CP and work above CP (W)
(Jones etal. 2010; Muniz-Pumares etal. 2019), and there-
fore slightly different predictions within the severe exercise
intensity domain; particularly at the extremes of the domain.
The 3-P CP model (Morton 1996) aimed to overcome these
limitations for short duration power outputs toward the upper
end of the severe and into the extreme exercise intensity
domain by incorporating Pmax as a model parameter, but it
still overestimates power outputs in the moderate exercise
intensity domain (see Fig.4).
Modelling power output belowthecritical power
The CP represents the theoretical asymptote of the power-
duration curve, suggesting that a given power output is infi-
nitely sustainable. However, this is clearly not the case for
real-world performances where exercise at the CP is limited
to 20–40min (Poole etal. 2016). For this reason, previ-
ous research (Peronnet and Thibault 1989; Puchowicz etal.
0306090120 150 180
Time (s)
PowerOutput (W)
Fig. 3 Sample data for the anaerobic power reserve model, black
dots—record power output over 5, 10, 15, 30, 60, 90, 120 and 150s
durations; horizontal black dashed line:—anaerobic power reserve;
green, blue and red dashed lines representing the power duration
curve with the rate constant (k) of the exponential decline in power
output (k = 0.024, k = 0.026, k = 0.027) according to Sanders and Hei-
jboer (2019b)
Table 1 Power-duration models corresponding to the respective exercise intensity domains
Equation1: P(t) power output, P(3-min) 3min field test, P(max) 1s peak power, e base of the natural logarithm (2.718), k the rate constant of the
exponential decline in power output, t time in seconds
Equation2: t time in seconds, Wʹ work above critical power, P power output, CP critical power, P(max) 1s peak power
Equation3: P(t) power output, Wʹ work above critical power, CP critical power, t time in seconds
Equation4: Pmap(t) power output at maximum aerobic power, MAPTTF time to task failure at maximum aerobic power, t time in seconds, A rep-
resents a fixed constant for the decline in power output over time, Ln natural logarithm to the base of e (2.718)
Equation5: P(t) power output, Wʹ work above critical power, CP critical power, t time in seconds, CPTTF time to task failure at critical power, A
represents a fixed constant for the decline in power output over time, Ln natural logarithm to the base of e (2.718)
Exercise intensity domains Model Equation
extreme Anaerobic power reserve
extreme and severe 3-parameter critical power model
Severe 2-parameter critical power model
extreme, severe and heavy Peronnet and Thibault model
(t)=MAP A×Ln
Omni power duration model
+CP A×Ln
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308 European Journal of Applied Physiology (2022) 122:301–316
1 3
2020) has suggested an exponential decay term below the
CP to predict power outputs in the heavy exercise intensity
domain (see Fig.2 and Eqs.4 and 5). However, these decay
terms are not necessarily routed in the underlying physiol-
ogy of fatigue in the heavy and moderate exercise intensity
domains (see Black etal. (2017), Clark etal. (2019) and
Amann (2011) for overviews of possible fatigue mecha-
nisms at these intensities). They do however represent the
best models to date for estimating exercise tolerance below
the CP (see equations in Table1).
Choosing amodelling approach
The authors recommend that coaches and practitioners refer
to the physiological demands of a given discipline or train-
ing modality to guide their choice. They should then select
the model that best predicts the power-duration relationship
across the range of intensities in which athletes will train and
race. For example, the two-parameter CP model (Moritani
etal. 1981; Whipp etal. 1982) overestimates both short-
and long-duration power outputs outside the severe exer-
cise intensity domain (see Fig.4), thus potentially limiting
its utility. To give some practical examples; power outputs
in the team sprint falls exclusively in the extreme exercise
intensity domain, whereas power outputs in the individual
pursuit falls within both the extreme and severe exercise
intensity domains (Gardner etal. 2005). In road cycling a
large proportion of the power output falls within the heavy
and moderate exercise intensity domains (van Erp and de
Koning 2019); however, power outputs in the extreme and
severe exercise intensity domains are more important in
predicting race performance (Menaspà etal. 2017). Longer
duration (ultra) endurance events, for example, ironman dis-
tance triathlons (Laursen 2011) or the ‘Race Across Amer-
ica’ (Hulton etal. 2010) fall within the moderate exercise
intensity domain, as do extensive training sessions in cycling
or triathlon (van Erp etal. 2020b; Laursen 2011). A differ-
ent modelling approach may be required for each of these
Interestingly some of the aforementioned models are able
to predict exercise tolerance in multiple exercise intensity
domains. Whilst there is a considerable body of evidence
indicating that the physiological responses in each exercise
intensity domain is unique (Burnley and Jones 2007), it
should be noted that most research is derived from exer-
cise intensities that are not in close proximity to the thresh-
olds that define a given exercise intensity domain. Work
by Pethick and colleagues (2020) looking at responses in
proximity to the critical torque (CT) during isolated knee
extension exercise, a proxy for CP, showed that above the
CT participants displayed physiological responses consist-
ent with the severe exercise intensity domain. Likewise,
slightly below the CT physiological responses associated
with the heavy exercise intensity domain were recorded.
Another pertinent example is that research has shown that
although the
O2 slow component is a defining characteris-
tic of the heavy exercise intensity domain, a variant of the
slow component, albeit smaller in magnitude, also occurs in
the moderate exercise intensity domain (Davies and Thomp-
son 1986). Whilst a proportion of the change in
O2 uptake
may be due to a shift in substrate utilisation, this change
wouldn’t account for the entire increase in
O2, suggesting
altered or additional muscle recruitment (Burnley and Jones
2018). Together, these findings suggest that rather than each
exercise intensity domain inducing distinct physiological
responses, there is instead a spectrum of responses across the
power-duration relationship (see Fig.2). Indeed, this would
explain why the power-duration curve is smooth in nature
and doesn’t contain ‘turn-points’ as would be expected if
the thresholds between exercise intensity domains were
indeed ‘hard’ in nature. It may also explain why some of
the aforementioned models are able to predict exercise toler-
ance across intensities in multiple exercise intensity domains
(Fig.2 and Table1).
Combining laboratory andeld testing
Both laboratory and field testing have been used in isola-
tion and in conjunction with each other to investigate physi-
ological and performance capacity in cycling (Gardner etal.
Power Output (W)
Fig. 4 Various power duration modelling approaches applied to
the same MMP data. MMP Mean Maximum Power, OmPD Omni
Power Duration model, P&T Peronnet and Thibault model, 2-P CP
two-parameter critical power model, 3-P CP three-parameter critical
power model; horizontal dashed line—critical power asymptote; ver-
tical dashed lines represent the approximate transitions between the
exercise intensity domains (extreme, severe, heavy and moderate)
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309European Journal of Applied Physiology (2022) 122:301–316
1 3
2007; Jobson etal. 2009; Jones and Vanhatalo 2017; Lucia
etal. 2001; Paton and Hopkins 2001).
In cycling, the most commonly reported measures from
laboratory testing include peak power output from sprint-
ing or graded incremental exercise tests,
O2max, %
MAP, fractional utilization of MAP, first and second lactate
or ventilatory thresholds, maximum lactate steady state and
cycling efficiency (Laurent etal. 2007; Lucia etal. 2000;
Mujika and Padilla 2001). Although good agreement exists
between some of these laboratory measures and cycling per-
formance, none of the aforementioned physiological vari-
ables can be used to create a power-duration relationship
as recommended by the authors for the purposes of power
As demonstrated before, a critical component of the
power-duration relationship is the border between the
heavy and severe exercise intensity domains; power outputs
at which a steady state can and cannot be achieved (Poole
etal. 2016; Poole etal. 1988). The physiological bound-
ary between these domains has been most associated with
endurance performance (Burnley and Jones 2007; Poole
etal. 1988). For a long time, the maximum lactate steady
state (MLSS) was considered as the gold standard for this
boundary (Billat etal. 2003; Keir etal. 2015; Kilding and
Jones 2005). However, recent work (Galán-Rioja etal. 2020;
Jamnick etal. 2020; Jones etal. 2019; Nixon etal. 2021) has
suggested that CP better estimates the maximal metabolic
steady state, the highest power output where a steady state
in the oxygen uptake (
O2) response can still be observed,
despite increasing blood lactate values (Bräuer and Smekal
2020). There is still some debate as to which method (if any)
is superior for differentiating between metabolic steady state
and non-steady state exercise, and whether both MLSS and
CP can actually be used interchangeably (Jones etal. 2019;
Keir etal. 2015; Nixon etal. 2021; Poole etal. 1988).
In applied settings, it has been suggested that an alter-
native approach, namely the functional threshold power
(FTP), can be used as a surrogate for the maximal meta-
bolic steady state: (Mackey and Horner 2021). FTP was
first described as the cycling power output that can be sus-
tained for one hour in a “quasi physiological steady-state”
(Bassett etal. 1999; Coggan 2003; Mackey and Horner
2021). FTP is therefore a surrogate of the 60min MMP.
It has been proposed that FTP can also be predicted either
by taking 95% of the power output in a 20-min maximal
field test (Borszcz etal. 2018; Morgan etal. 2019; Valen-
zuela etal. 2018) or by taking 90% of the power output
in a 8-min maximal field test (Sanders etal. 2020); the
former being commonly used (Valenzuela etal. 2018). In
contrast to CP and MLSS, where multiple determination
trials are required, FTP can be predicted from a single trial
and is, therefore, less time consuming. This time efficient
approach may explain why the concept has been widely
adopted in cycling (Mackey and Horner 2021). However,
whilst CP and MLSS can be considered as estimates of the
maximal metabolic steady state (Keir etal. 2015; Poole
etal. 1988), this cannot be confirmed for FTP (Morgan
etal. 2019). Whilst both MLSS and FTP are single-param-
eter estimates, the CP concept can be used to predict TTF
for a range of power values within the severe exercise
intensity domain and provides an estimate of the border
between metabolic steady state and non-steady state exer-
cise. The same cannot be said for either MLSS or FTP,
which can only predict a single point on the power-dura-
tion relationship, or a border between exercise intensity
domains, but not TTF for a range of power output values.
Physiologically speaking, CP has been shown to represent
the highest power output at which there is no progressive
derangement in the muscle metabolite milieu (Burnley and
Jones 2018); however, instead of a ‘hard’ border, the CP
represents a phase transition between the heavy and severe
exercise intensity domains (Pethick etal. 2020). Mitchell
and colleagues (2018) also reported a strong relationship
between CP and muscle capillary density, underpinning the
aerobic component of CP. Similarly, Vanhatalo etal. (2016)
demonstrated that CP was strongly associated with the per-
centage of highly oxidative type I muscle fibres. Above CP,
in the severe exercise intensity domain a non-metabolic
steady state occurs, characterized by a reduction in intra-
muscular creatine phosphate stores, continuously increasing
concentrations of inorganic phosphate, hydrogen ions and
blood lactate, which are all associated with a reduced con-
tractile function of the working muscle (Allen etal. 2008;
Burnley and Jones 2007, 2018; Jones etal. 2010; Poole etal.
2016, 1988).
Although a strong relationship exists between FTP and
CP estimates (Denham etal. 2020; Karsten etal. 2020; Mor-
gan etal. 2019, Mackey and Horner 2021), and FTP and
MLSS (Borszcz etal. 2019), the cited studies have demon-
strated that the limits of agreement between parameters are
too large for them to be used interchangeably. This ques-
tions the relevance of FTP (Borszcz etal. 2018; Karsten
etal. 2020; Morgan etal. 2019; Valenzuela etal. 2018).
Furthermore, Borszcz and colleagues (2018) demonstrated
that the 95% of 20min power output overestimates 60min
power output, and recommended that 20min power output
alone should be used for training prescription and perfor-
mance monitoring, rather than trying to make estimates
of 60min power output (i.e. FTP). After all, both 20 and
60min power output are arbitrary in nature. However, whilst
FTP might represent an arbitrary value, rather than a physi-
ological threshold, it may still have practical utility in terms
of informing the training process (Valenzuela etal. 2018).
However, to the best of the authors’ knowledge no studies
exist that compare performance outcomes when prescribing
training based on different concepts, i.e. FTP, CP and MLSS.
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310 European Journal of Applied Physiology (2022) 122:301–316
1 3
That said, for the reasons outlined above the authors
consider CP the most useful concept in terms of deriving a
power-duration relationship, and therefore recommend the
use of the CP concept in the field of power profiling.
Deriving theparameters ofpower‑duration
There is currently no consensus on how best to derive the
parameters that are needed to model the power-duration rela-
tionship; namely Pmax, CP and W. Likewise, there is consid-
erable debate on which mathematical model should be used
to derive CP and W (Maturana etal. 2018; Muniz-Pumares
etal. 2019; Nimmerichter etal. 2020).
Traditionally, performing three to five prediction trials
between 2 and 15min of duration (Karsten etal. 2015; Mat-
urana etal. 2018; Muniz-Pumares etal. 2019) allows CP and
W to be derived through weighted least square or geometric
mean linear and nonlinear regression analysis (Vinetti etal.
2017; Vinetti etal. 2020). Prediction trials shorter than 2min
do not ensure the attainment of
O2max (i.e. they fall outside
the severe intensity domain) (Hill and Smith 1994; Maturana
etal. 2018; Muniz-Pumares etal. 2019; Nimmerichter etal.
2020), while prediction trials longer than 15min are not
recommended due to the influence of glycogen depletion and
psychological factors (i.e. motivation) (Karsten etal. 2015;
Maturana etal. 2018). To avoid any skewness during the
mathematical modelling and reduce errors in the calculation
of CP and W the shortest prediction trial should last between
2 and 5min and the longest prediction trial between 12 and
15min (Karsten etal. 2015; Maturana etal. 2018; Muniz-
Pumares etal. 2019). Inter-trial recovery between prediction
trials should be set to a minimum of 30min during a single
visit or 24h during multiple days (Karsten etal. 2017). The
benefit of multiple days if that any fatigue induced by the
initial prediction trial does not affect the subsequent one, but
possible error due to day-to-day variation in power output
is introduced.
Once the performance trials have been completed the
respective power output and trial duration values can be
used to derive CP and W. Computing CP and W estimates
from a nonlinear two- or three-parameter models requires
access to statistical software to perform a weighted least
square or geometric mean regression analysis (Vinetti
etal. 2017, 2020). To simplify this process for coaches
and practitioners there are two options available to lin-
earize the hyperbolic power-duration relationship (see
Fig.5). Practitioners can either use a) the linear work time
CP model (see Eq.3 and Fig.5c or b) the linear power
inverse of time CP model (see Eq.3 and Fig.5b), where
CP and W can be derived as the slope and intercept of
the linear relationship (Clarke and Skiba 2013; Sreedhara
etal. 2019). All mathematical models from Fig.5 provide
a high accuracy for the model fit, but there is a possibility
that the power-duration parameter estimates (CP and W)
diverge somewhat depending on which fitting method is
used (Muniz-Pumares etal. 2019). As a result, Hill (1993)
suggested that the best fit mathematical model could be
more objectively selected, where the model producing the
lowest standard error of the estimate (SEE) should be the
0200 400600 800
Time (s)
0.0000.002 0.0040.006 0.0080.010
0200 400600 800
Time (s)
Work (J)
Fig. 5 Graphical illustration of the power-duration relationship for the
hyperbolic (a), inverse of time (b) and linear work time (c). Model
adopted from Clarke and Skiba (2013)
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311European Journal of Applied Physiology (2022) 122:301–316
1 3
preferred way to derive CP (Hill 1993; Muniz-Pumares
etal. 2019).
CP and W parameter estimates can also be derived using
only two prediction trials (Parker Simpson and Kordi 2017).
While this can be seen as a time-efficient testing protocol,
the limitation of this approach is that the linear relation-
ship always results in a perfect fit (R2 = 1.0). In addition, no
parameters for the goodness of fit (i.e., SEE) can be derived.
Therefore, it is recommended to use at least three prediction
trials to ensure a low standard error for CP (2–5%) and W
(< 10%) (Black etal. 2016; Dekerle etal. 2015). Performing
three prediction trials and using a two-parameter CP model
to fit the data results in one degree of freedom. For instance,
a standard error of 5W for a cyclist with a CP of 385W
would then need to be multiplied by 12.7 to calculate the
95% confidence limits (± 64W) in both directions. Adding a
fourth prediction trial would reduce the CP standard error to
3W and the 95% confidence limits (± 38W) thus improving
the CP predictive ability.
The 3-min all out test has also been proposed as a more
time efficient way to derive CP and W (Vanhatalo etal.
2007, 2008). The principal assumption in this test is that W
or more accurately WEP (work above end test power) as it is
known in this test, is fully depleted within the first 150s and
therefore during the last 30s only CP (end test power) can
be sustained. Despite showing good reliability and validity
compared with traditional CP testing in some circumstances
(Wright etal. 2017), other research in elite cyclists shows
significantly higher CP estimates are derived from the 3-min
all out test than traditional protocols (McClave etal. 2011)
which can lead to overestimation of performance capacity
in the severe exercise intensity domain (Nicolò etal. 2017).
This finding brings into question whether the 3-min all out
test can be used in the field of power profiling.
In some power-duration models (see Table1) Pmax is
an additional input parameter when modelling the power-
duration relationship. Extensive research (Douglas etal.
2021; Driss and Vandewalle 2013; McCartney etal. 1983,
1985) was conducted on the assessment and mechanisms of
Pmax in cycling (Sargeant etal. 1981). Assessing Pmax in the
laboratory or field settings requires a thoughtful reflection
on testing protocols. Recent research used the highest 1s
power output within 4s, 10s and 15s sprints to derive Pmax
(Driss and Vandewalle 2013; Ferguson etal. 2021; Gardner
etal. 2007; Sanders and Heijboer 2019b). If efforts longer
than 10s are used Pmax could be negatively influenced as the
cyclist may apply a pacing strategy (Driss and Vandewalle
2013; Gardner etal. 2007). Practitioners should also be
aware of a “learning effect” during all-out sprint efforts, and
it is therefore recommended that adequate familiarization is
undertaken prior to formal testing of Pmax. Additional impor-
tant factors to consider when testing Pmax in a laboratory
setting are; the torque factor setting (Forbes etal. 2014) and
whether the expected Pmax is within the range of validity of
the power measuring device. For example, a commercially
available smart trainer is only valid up to 700W, which is
much lower than the expected Pmax for some populations
(Zadow etal. 2016).
Ecological validity
Cadence, body position as well as topography, i.e. level
ground or uphill conditions, have also been shown to influ-
ence model parameter estimates due to different biomechani-
cal recruitment patterns (Bertucci etal. 2005; Kordi etal.
2019; Nimmerichter etal. 2012). Therefore, rider specializa-
tion (for example climber vs. time trial specialist) and race
demands (uphill vs. flat, on-road vs. off-road, etc.) need to
be considered in the selection of testing environments (Nim-
merichter etal. 2012). The testing conditions should mirror
the conditions in which athletes are expected to perform.
For example, it is recommended that time trial specialists
perform prediction trials on a time trial bike on level ground,
while climbing specialists conduct testing in uphill condi-
tions on a road bike.
Previous research has also investigated whether time
trials or TTF trials should be favoured as prediction trials
(Coakley and Passfield 2018; Karsten etal. 2018). Tradi-
tionally, TTF trials have been based on a fixed percentage
(i.e. 80–105%) of the power output in a GXT. The main
limitation with this approach being that inter-individual dif-
ferences could influence the trial duration (Jamnick etal.
2020). In contrast, maximum effort time trialling requires a
high level of pacing ability and may therefore only be suit-
able for use with experienced cyclists (Karsten etal. 2018).
However, time trials are inherently easier to perform in field
settings, as Simpson and Kordi (2017) have shown a particu-
larly time-effective protocol using time trials in elite athletes
can produce valid CP and W´ estimates. However, in less
trained participants higher power output values have been
reported in TTF trials resulting in higher CP and W´ estima-
tions (Coakley and Passfield 2018).
As mentioned above, environmental factors should be
considered whenever performing any formal testing. Testing
conditions during formal testing should therefore aim to mir-
ror as closely as possible the competition settings to ensure
environmental validity. To illustrate this point, CP has been
shown to decline significantly as altitude increases, while
W only decreased above 4.000m of altitude (Townsend
etal. 2017); heat and humidity have been shown to influ-
ence power outputs in formal testing (Racinais etal. 2015).
Previous research also investigated the influence of
cadence on time trial performance and power-duration
parameter estimates. While CP estimates were higher at
cadences 60 vs. 100 revolutions per minute in recreation-
ally trained individuals (Barker etal. 2006; Carnevale and
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312 European Journal of Applied Physiology (2022) 122:301–316
1 3
Gaesser 1991), no statistically significant differences in
physiological determinants (gross efficiency, energy turno-
ver) were reported at cadences between 80 vs. 100 revolu-
tions per minute in elite cyclists during cycling time trials
(Foss and Hallén 2005). Although higher power outputs can
be achieved at lower cadences, elite cyclists tend to prefer
higher cadences around ~ 90 revolutions per minute despite
reductions in cycling efficiency.
Agreement betweenmodelled
power‑duration relationship andMMP
Good agreement between CP estimates derived from for-
mal testing and MMP values has been reported (Leo etal.
2020, 2021a; b; Nimmerichter etal. 2020; Quod etal. 2010).
While a good agreement between CP derived from formal
testing and racing has been shown, the same cannot be
confirmed for W. Both Leo etal. (2020) and Karsten etal.
(2015) reported low agreement between W´ derived from
formal testing and MMP data. This low agreement may be
due to cyclists not performing maximal efforts in race situ-
ations apart from very specific circumstances (i.e., during
time trials or at the finish of races). If cyclists were to fully
deplete W´ in any other circumstance (i.e. uphill mountain
finish, lead out or tine trial), there is a chance that they may
subsequently not be able to match the power requirement to
follow the peloton. These scenarios have direct implications
on the recorded MMP values thereafter, as they are not as
high as the MMP values recorded earlier in the race (Leo
etal. 2021b). Thus, these efforts are not being captured via
basic MMP analysis per se.
Good agreement has been reported between power out-
puts predicted by the APR model and race-derived MMP
data for short duration power outputs (< 2min) in profes-
sional male cyclists (Sanders etal. 2017; Sanders and Heij-
boer 2019b). However, only limited research exists to verify
if this approach could also be applied to other populations.
Future directions
Although many approaches concerning power profiling have
been developed in the literature, it remains unclear which
approach provides the greatest insight. Arguably, the most
convenient way for practitioners to create a power profile
would be to retrospectively use field derived MMP data
from training and racing over pre-defined durations (Ebert
etal. 2005; Menaspà etal. 2017; Sanders and van Erp 2021;
Vogt etal. 2007b). Although this kind of data may provide
valuable insights into racing demands in highly trained
cyclists, little information can be retrieved in terms of the
power-duration relationship due to the arbitrary selection of
MMP values.
Deriving a comparative measure allows longitudinal
analysis: for example, if a rider records a MMP value in
racing which exceeds the prediction from formal testing,
practitioners can use that information to monitor changes
in the power profile. However, deriving W´ from racing or
field testing has shown poor predictive ability (Karsten etal.
2015; Leo etal. 2021a) questioning the practical utility of
W for power profiling purposes. When creating a theoreti-
cal power-duration curve from formal testing, care should
be taken that the appropriate models are used. For example,
application of the CP concept outside the severe exercise
intensity domain involves an overestimation in short MMP
(< 2min) ability and long duration MMP (> 40min) sus-
tainability. For this reason, the APR model provides a use-
ful concept to predict the power-duration relationship in the
extreme exercise intensity domain.
While the power-duration relationship in the severe exer-
cise intensity domain has been well investigated based on
the CP concept (Jones etal. 2010; Poole etal. 2016), limited
research exists on deriving the power-duration relationship
in the moderate and heavy exercise intensity domains (Black
etal. 2017). Hence Puchowicz etal. (2020) and Peronnet
and Thibault (1989) proposed mathematical models with an
aerobic decay term, but limited research exists to assess if
these concepts have a high predictive ability for the power-
duration relationship in the moderate and heavy exercise
intensity domains in relation to the muscle bioenergetic sys-
tem (Korzeniewski 2019; Korzeniewski and Rossiter 2020,
2021; Vanhatalo etal. 2016).
Recent work (van Erp etal. 2021b; Leo etal. 2021b) has
shown a reduction in MMP values as prior work increases.
However, future research is needed to better understand the
mechanisms which lead to alterations in the power-dura-
tion due to fatigue, especially the influence of the exercise
intensity and if work in different exercise intensity domains
induce the same degree of downward shift in the power-
duration curve. This is important as improved performance
capacity, i.e. smaller alterations in the power-duration rela-
tionship, has been positively related to race success (van Erp
etal. 2021b; Leo etal. 2021b).
In the era of big data science a novel approach intro-
duced by Puchowicz (2018) on the Golden Cheetah open
data project (Liversedge 2020) could provide novel insights
into power profiling. Functional principal component anal-
ysis (FPCA) enables an in-depth view of the components
of variability in MMP data between cyclists via eigenfunc-
tions which classify riders on their phenotype (sprinter vs.
climber) and performance level. Currently, however, the use
of FPCA for the purposes of power profiling still requires
adequate scientific validation before any potential findings
can be applied by coaches and practitioners.
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313European Journal of Applied Physiology (2022) 122:301–316
1 3
Practical recommendations inapplied
Based on the current literature and the authors’ experience
conducting power profiling in applied settings, the fol-
lowing recommendations can be made as a starting point
for coaches and practitioners: to derive the parameters
to model a power-duration curve a formal test protocol
should include one sprint effort (i.e. ~ 10–15s) and at least
three maximum efforts between 2 and 15min (Karsten
etal. 2015; Leo etal. 2021a; Muniz-Pumares etal. 2019;
Sanders and Heijboer 2019b). These efforts can be com-
pleted in a single testing session, though it is recom-
mended to divide field testing into two sessions over two
consecutive days. The order of efforts should preferably be
randomized for scientific research or follow the cyclist’s or
coach’s individual preference in applied settings. Inter-trial
recovery between efforts should be set to a minimum of
30min of active recovery (< 2 rating of perceived exer-
tion) (Karsten etal. 2017). CP and W should be derived by
the non-linear two-parameter CP model (Muniz-Pumares
etal. 2019), while Pmax should be referred to the 1s peak
power during the ~ 10–15s sprint effort (Sanders and Hei-
jboer 2019b). This protocol will allow coaches and practi-
tioners to derive valid Pmax, CP and W estimates. Coaches
can then choose the best modelling approach based on the
exercise intensity domain(s) that are important for race
analysis and training prescription in a given discipline.
Power meters should be verified for accurate and reli-
able measurement and a zero-offset or re-calibration
according to the manufacturer’s recommendations is
The authors do not recommend using single effort field
tests (i.e. 8min or 20min TT) to derive the FTP estimate
because it lacks physiological background and only rep-
resents a single point on the power-duration curve. Nor
do they recommend the use of the 3- min all-out test as
this may lead to an overestimation of the power-duration
relationship in the severe exercise intensity domain.
To increase the ecological validity of power profiling
we recommend a careful selection of the power-duration
modelling approach, based on biomechanical and physi-
ological principles. Standardized laboratory and field test-
ing should be conducted in line with performance analysis
from training and racing to increase the practical utility of
performance prediction and training related consequences.
In addition, any formal testing should consider the envi-
ronmental and topographical conditions in which the power
profile information is to be applied in. Therefore, the dura-
tion of the effort, gradient, inter-trial recovery, rider type
specialization (climbers vs. flat specialist) and race demands
(climb vs. time trial) should be replicated as best possible.
Collectively, power profiling provides an advanced
opportunity for performance modelling based on power
output data from training and racing in combination with
traditional laboratory and field-testing methods to maximize
cycling performance.
Acknowledgements We would like to thank Dr. Richard Ferguson and
Mag. Dieter Simon for their theoretical input and assistance in real-
izing this project.
Author contributions PL, JS and IM had the idea of the article. PL, JS
and TP performed the literature search and writing and and graphical
illustration. JS, TP, JL and IM critically revised the manuscript.
Funding Open access funding provided by University of Innsbruck
and Medical University of Innsbruck. No funding was received for the
preparation of this manuscript.
Availability of data and material Not applicable.
Code availability Not applicable.
Conflict of interest Author P.L., J.S., T.P., J.L. and I.M. declare that
they have no conflict of interests.
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication All authors agree to the publication of the
submitted manuscript.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
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copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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... The first registry of pedaling power output in cycle ergometer was dated in 1896, but the first portable power meters were not designed until the end of the 1980s (i.e., SRM, Balboa Instrument PowerPacer and Look Max One) [1,2]. Since then, these devices have been used to monitor training, to perform field-based performance tests, to analyze cycling competitions, and to evaluate changes in bicycle equipment [3]. ...
... Furthermore, Bouillod et al. [5] demonstrated that vibration and field conditions affect the power output measured. This latter could condition the analysis and interpretation of both exercise intensity zones and power output profile of the cyclists [2,5,7], and their critical power [8,9]. These variables are widely used to quantify the competition load and to plan training [2]. ...
... This latter could condition the analysis and interpretation of both exercise intensity zones and power output profile of the cyclists [2,5,7], and their critical power [8,9]. These variables are widely used to quantify the competition load and to plan training [2]. However, to the best of our knowledge, no study has compared the influence of the power meter on these types of analysis during competition, possibly due to the mass added by each power meter or to the conflict of interest between sponsors (i.e., normally each cycling team uses only one power meter brand). ...
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Various power meters are used to assess road-cycling performance in training and competition, but no previous study has analyzed their interchangeability in these conditions. Therefore, the purpose was to compare the data obtained from two different power meters (PowerTap vs. Power2Max) during cycling road races. A national-level under-23 male competitive cyclist completed six road-cycling official competitions (five road races and one individual time trial), in which power output was simultaneously registered with the two power meters. After this, the main power output variables were analyzed with the same software. The average and critical power obtained from the PowerTap power meter were slightly lower than from the Power2Max power meter (3.56 ± 0.68 and 3.62 ± 0.74 W·kg−1, 5.06 and 5.11 W·kg−1, respectively), and the correlations between both devices were very high (r ≥ 0.996 and p < 0.001). In contrast, the PowerTap power meter registered a significantly higher (p < 0.05) percentage of time at <0.75 and >7.50 W·kg−1 and power profile at 1, 5 and 10 s. In conclusion, the data obtained in competitions by the two power meters were interchangeable. Nevertheless, the Power2Max power meter underestimated the pedaling power during short and high-intensity intervals (≤10.0 s and >7.50 W·kg−1) compared to the PowerTap power meter. Therefore, the analysis of these efforts should be treated with caution.
... Performance in long-lasting cycling races can be evaluated with several laboratory-derived and field-derived parameters (18,30). Among them, the functional threshold power (FTP) has been defined as 95% of the average power output (PO) during a 20-minute time trial (TT) in the field measured with a power meter and has been proposed as a surrogate of the maximal PO sustainable for 1 hour (1). ...
... Moreover, although it was intended to be a field test, studies on the physiological underpinnings of FTP were mostly confined to the laboratory setting, where mixed agreement was found with a 60-minute TT (3,20,23), as well as the ventilatory compensation point (2,33), the individual anaerobic threshold (3,20), the Dmax lactate threshold (23,32,35), the 4 mM (16,32) lactate threshold (P 4mM ), the maximal lactate steady state (4,15,19), and the critical power (CP) (17,24,27), with most studies refuting interchangeability. The 20-minute TT naturally evokes the concept of PO-duration (T lim ) relationship (18), its simplest hyperbolic form being T lim 5 W9/(PO-CP), where the curvature constant W9 (the amount of work that can be performed above CP) interestingly resulted unrelated to FTP (27). The need of further studies assessing FTP in outdoor conditions was recently highlighted (21), since only 2 studies have investigated the outdoor FTP test to date (both with the modified protocol of 90% of the average PO of an 8-minute TT): 1 (11) reported an FTP not significantly different from P 4mM and the other (31) an FTP greater but significantly related to P 4mM . ...
... Exercise intensity is one of many factors underpinning the levels of central and peripheral fatigue after a whole-body exercise [7][8][9]. Exercise intensity is commonly divided into three exercise-intensity domains: moderate-, heavy-, and severe-intensity domains [10,11]. The moderate-intensity domain is characterized by exercise intensities below the gas exchange threshold (GET) [12]. ...
Objectives. — Whether performance fatigability and its determinants (i.e., peripheral and central fatigue) after cycling exercise performed at extreme- and severe-intensity domains are of similar magnitude is unknown. In the current study, we investigated the levels of performance fatigability and peripheral and central fatigue in nine young female after a cycling exercise performed until the limit of tolerance at extreme- (i.e., 140% of peak power output) and severe-intensity domains (80% of the difference between gas exchange threshold and peak power output). Equipment and methods. — The level of maximal voluntary isometric contraction (MVC), potentiated quadriceps twitch force evoked by single pulse (Q tw ), and voluntary activation (VA) were measured pre- and post-exercise. Results. — The MVC (a marker of performance fatigability) decreased from pre- to post-exercise (P < 0.05) in the extreme- (−10 ± 8%) and severe-intensity (−10 ± 10%) exercises. The Q tw (a marker of peripheral fatigue) reduced similarly from pre- to post-exercise (P < 0.05) in the extreme- (−18 ± 15%) and severe-intensity (−10 ± 17%) exercises. The VA (a marker of central fatigue) did not reduce from pre- to post-exercise in either severe- (2 ± 7%) or extreme-intensity (−2 ± 7%) exercises (P > 0.05). Conclusions. — These findings suggest a similar amount of performance fatigability and peripheral fatigue after severe- and extreme-intensity cycling exercises in young female, which is in accordance with the concept that exercises performed above critical power until task failure attain a common level of peripheral fatigue regardless of exercise intensity.
... The process of performance profiling consists of retrieving an athlete's performance/duration points and fitting curves (Leo et al., 2022). One strategy is to build the performance profile using the CP model, i.e., using repeated maximal efforts to exhaustion at constant power output. ...
... The record power profile (RPP) is the highest power output (PO) that can be achieved by a cyclist for a given duration under fieldbased (training or racing) conditions (5). Since the development of PO sensors, the RPP has gained attention as a means to monitor cyclists' performance and to perform comparisons across different cycling categories or team roles (10). ...
Muriel, X, Hernández-Belmonte, A, Mateo-March, M, Valenzuela, PL, Zabala, M, Barranco-Gil, D, Lucia, A, and Pallares, JG. Is the record power profile repeatable? A practical analysis and interpretation in professional cyclists. J Strength Cond Res XX(X): 000-000, 2022-This study assessed the repeatability of the Record Power Profile (RPP, i.e., the highest power output that a cyclist can attain for different effort durations under field-based conditions). We registered the RPP of 12 professional cyclists (age 32 6 5 years) for efforts lasting between 30 seconds and 60 minutes during 3 periods of a season, each of 23-day duration: preparation (including training data only), specific (training and competition data), and competition (competition data only) periods. Repeatability was assessed using the highest 2 (RPP 2), 3 (RPP 3), and 5 (RPP 5) values of mean maximum power obtained by the cyclists for each effort duration in each of the 3 periods. Smaller standard errors of measurement (SEM) were found as the competitive period approached, especially for short-duration efforts (i.e., 30 seconds, 1 minute, and 5 minutes, where SEM ranged from 4.3 to 12.5%, 4.1-8.5%, and 2.6-7.0% in the preparation, specific, and competition periods, respectively). However, similar SEM values were found in the 3 periods for RPP 2 , RPP 3 , or RPP 5. In conclusion, the RPP appears as a repeatable parameter for monitoring field-based performance within the different phases of the season in professional cyclists.
... As a result, professional cyclist are usually monitored by directly using the data that is collected during their rides. Here, the main focus is on the exercise intensity and in particular on the produced power (Leo et al. 2021). A frequently used feature is the so-called power duration curve (Hunter et al. 2019). ...
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We present a personalized approach for frequent fitness monitoring in road cycling solely relying on sensor data collected during bike rides and without the need for maximal effort tests. We use competition and training data of three world-class cyclists of Team Jumbo–Visma to construct personalised heart rate models that relate the heart rate during exercise to the pedal power signal. Our model captures the non-trivial dependency between exertion and corresponding response of the heart rate, which we show can be effectively estimated by an exponential kernel. To construct the daily heart rate models that are required for day-to-day fitness estimation, we aggregate all sessions in the previous week and apply sampling. On average, the explained variance of our models is 0.86, which we demonstrate is more than twice as large as for models that ignore the temporal integration involved in the heart’s response to exercise. We show that the fitness of a cyclist can be monitored by tracking developments of parameters of our heart rate models. In particular, we monitor the decay constant of the kernel involved, and also analytically determine virtual aerobic and anaerobic thresholds. We demonstrate that our findings for the virtual anaerobic threshold on average agree with the results of exercise tests. We believe this work is an important step forward in performance optimization by opening up avenues for switching to adaptive training programs that take into account the current physiological state of an athlete.
... Cycling performance is determined by many physiological factors, including maximal oxygen uptake (VO 2 max) and peak power output (PPO). 1,2 Emerging data have been recently published 1,3 to compare elite junior, under-23 (U23), and professional cyclists. ...
Objective: To determine whether the altitude of birth/childhood influences the values in peak power output (PPO) and estimated maximum oxygen uptake (estVO2max) in male Colombian road cyclists of different performance levels. This study also aimed to determine whether cyclists born at high altitudes tend to be more successful. Methods: Eighty riders aged between 17 and 22 years of 3 performance levels (U23 world-class level, WC, n = 8; U23 national level, N23, n = 41; junior national level, J, n = 31) and 3 altitude levels (<800 m, low; 800–2000 m, moderate; >2000 m, high) performed an ergocycle maximal incremental test to exhaustion at an altitude of 2570 m. Results: Altogether, while cyclists born at an altitude >2000 m represented ∼50% of the analyzed sample, there was a significantly higher proportion (84%) of these cyclists who had participated as professionals in a Grand Tour (χ2[1, N = 80] = 4.58, P < .05). Riders of the low group had lower values of PPO and estVO2max than cyclists of moderate and high altitudes, while no differences were noted between moderate- and high-altitude groups. In N23, PPO and estVO2max were lower in the low- than in the moderate-altitude group, while in the J cyclists, PPO and estVO2max were lower in the low-altitude compared with both moderate- and high-altitude groups. Discussion: Among the cyclists tested at altitude in junior and U23, there is an overrepresentation of individuals who reached an elite level and were born at a high altitude (>2000 m). As no clear differences were observed between moderate- and high-altitude cyclists, the higher prevalence of elite cyclists in the latter group may originate from various—still unclear—mechanisms.
This chapter is mainly devoted to an analysis of the exercise transients, along the paths traced by Margaria in 1933. The oxygen deficit contracted during light exercise at the expense of anaerobic alactic metabolism (obligatory component of the oxygen deficit), the role of oxygen stores and early lactate in the oxygen deficit, especially at higher work loads, the metabolic control of muscle oxygen consumption during exercise and the slow component of its kinetics at the onset of exercise are discussed. A more detailed description of anaerobic metabolisms is carried out in the second part. Blood lactate accumulation in submaximal exercise, the energy equivalent of lactate and the maximal lactate power are analyzed. The maximal explosive power and the power and capacity of anaerobic alactic power are then discussed. In the appendix, a detailed analysis of the concept of anaerobic threshold is proposed along the energetic way of thinking of the School of Milano.
This study aimed to compare different models of Wʹ balance (Wʹbal) during road cycling races. These models allow tracking of the depletion and reconstitution of energy reserves during intermittent exercises. Fifteen high-level cyclists shared a year of data, including the power output and GPS records. They then selected a race during which they were in very good condition, and where they reached a maximum level of exhaustion in the final phase. Based on their seasonal data, their critical power and Wʹ were calculated and incorporated into the integral and differential models of Skiba and Bartram model. Wʹbal was calculated throughout the races and normalised to Wʹ. The integral, differential, and Bartram models predicted a mean Wʹbal of 41 ± 27%, 67 ± 17%, and 82 ± 9%, a minimum Wʹbal of −21 ± 28%, −3 ± 22%, and 22 ± 24%, and a Wʹbal at the identified point of exhaustion of 0 ± 30%, 21 ± 28%, and 42 ± 23% (all significantly different, p < 0.01). Given the large differences between the models and the high inter-individual variability of Wʹbal at the participant-identified point of exhaustion, this study shows that the models are not interchangeable and that the values of Wʹbal should be considered with caution when analysing races.
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This study investigates associations between power at several durations to show inter-relationships of power across a range of durations in sprint track cyclists. The currently-accepted hypothesis peak power holds a near perfect relationship with sprint performance, and thus a near 1:1 slope with power at sprint durations up to 30-s, is tested. The equally well-accepted and complementary hypothesis there is no strong association with power over longer durations is also tested. 56 data sets from 27 cyclists (21 male, 6 female) provided maximal power for durations from 1-s to 20-min. Peak power values are compared to assess strength of correlation (R2), and any relationship (slope) across every level. R2 between 15-s – 30-s power and durations from 1-s to 20-min remained high (R2 ≥ 0.83). Despite current assumptions around 1-s power, our data shows this relationship is stronger around competition durations, and 1-s power also still shared strong relationships with longer durations out to 20-min. Slopes for relationships shorter durations were closer to a 1:1 relationship than longer durations, but closer to long-duration slopes than to a 1:1 line. The present analyses contradicts both well-accepted hypotheses, and the concept of peak power being a primary metric for sprint cycling, based on very strong relationships to power from durations commonly associated with oxidative energetic pathways, as well as short durations. This study shows the importance and potential of training durations from 1-s to 20-min over a preparation period to improve competition sprint cycling performance.
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The metabolic boundary separating the heavy-intensity and severe-intensity exercise domains is of scientific and practical interest but there is controversy concerning whether the maximal lactate steady state (MLSS) or critical power (synonymous with critical speed, CS) better represents this boundary. We measured the running speeds at MLSS and CS and investigated their ability to discriminate speeds at which $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 was stable over time from speeds at which a steady-state $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 could not be established. Ten well-trained male distance runners completed 9–12 constant-speed treadmill tests, including 3–5 runs of up to 30-min duration for the assessment of MLSS and at least 4 runs performed to the limit of tolerance for assessment of CS. The running speeds at CS and MLSS were significantly different (16.4 ± 1.3 vs. 15.2 ± 0.9 km/h, respectively; P < 0.001). Blood lactate concentration was higher and increased with time at a speed 0.5 km/h higher than MLSS compared to MLSS ( P < 0.01); however, pulmonary $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 did not change significantly between 10 and 30 min at either MLSS or MLSS + 0.5 km/h. In contrast, $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 increased significantly over time and reached $$\dot{V}{\text{O}}_{2\,\,\max }$$ V ˙ O 2 max at end-exercise at a speed ~ 0.4 km/h above CS ( P < 0.05) but remained stable at a speed ~ 0.5 km/h below CS. The stability of $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 at a speed exceeding MLSS suggests that MLSS underestimates the maximal metabolic steady state. These results indicate that CS more closely represents the maximal metabolic steady state when the latter is appropriately defined according to the ability to stabilise pulmonary $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 .
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Functional Threshold Power (FTP) in cycling is increasingly used in exercise prescription, particularly with the rise in use of home trainers and virtual exercise platforms. FTP testing does not require biological sampling and is considered a more practical test than others. This scoping review investigated what is known about the 20-minute FTP (FTP²⁰) test. A three-step search strategy was used to identify studies in relevant databases (PubMed, CINAHL, SportDiscus, Google Scholar, Web of Science) and grey literature. Data were extracted and common themes identified which allowed for descriptive analysis and thematic summary. Fifteen studies were included. The primary focus fitted broadly into four themes: reliability, association with other physiological markers, other power-related concepts and performance prediction. The FTP²⁰ test was reported as a reliable test. Studies investigating the relationship of FTP²⁰ with other physiological markers and power-related concepts reported large limits of agreement suggesting parameters cannot be used interchangeably. Some findings indicate that FTP²⁰ may be useful in performance prediction. The majority of studies involved trained male cyclists. Overall, existing literature on the FTP²⁰ test is limited. Further investigation is needed to provide physiological justification for FTP²⁰ and inform use in exercise prescription in a range of populations.
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Maximal muscular power production is of fundamental importance to human functional capacity and feats of performance. Here, we present a synthesis of literature pertaining to physiological systems that limit maximal muscular power during cyclic actions characteristic of locomotor behaviours, and how they adapt to training. Maximal, cyclic muscular power is known to be the main determinant of sprint cycling performance, and therefore we present this synthesis in the context of sprint cycling. Cyclical power is interactively constrained by force-velocity properties (i.e. maximum force and maximum shortening velocity), activation-relaxation kinetics and muscle coordination across the continuum of cycle frequencies, with the relative influence of each factor being frequency dependent. Muscle cross-sectional area and fibre composition appear to be the most prominent properties influencing maximal muscular power and the power-frequency relationship. Due to the role of muscle fibre composition in determining maximum shortening velocity and activation-relaxation kinetics, it remains unclear how improvable these properties are with training. Increases in maximal muscular power may therefore arise primarily from improvements in maximum force production and neuromuscular coordination via appropriate training. Because maximal efforts may need to be sustained for~15-60 s within sprint cycling competition, the ability to attenuate fatigue-related power loss is also critical to performance. Within this context, the fatigued state is characterised by impairments in force-velocity properties and activation-relaxation kinetics. A suppression and leftward shift of the power-frequency relationship is subsequently observed. It is not clear if rates of power loss can be improved with training, even in the presence adaptations associated with fatigue-resistance. Increasing maximum power may be most efficacious for improving sustained power during brief maximal efforts, although the inclusion of sprint interval training likely remains beneficial. Therefore, evidence from sprint cycling indicates that brief maximal muscular power production under cyclical conditions can be readily improved via appropriate training, with direct implications for sprint cycling as well as other athletic and health-related pursuits. Maximal muscle power production under cyclical conditions is interactively constrained by force-velocity properties, activation-relaxation kinetics and muscle coordination across the continuum of possible movement frequencies. Fatigue alters the power-frequency relationship, with a higher degree of power loss at higher movement frequencies. Maximal muscular power production can be readily increased with appropriate strength and power training; it remains less clear if rates of power loss during brief maximal sustained efforts can be improved with training.
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Some teams aiming for victory in a mountain stage in cycling take control in the uphill sections of the stage. While drafting, the team imposes a high speed at the front of the peloton defending their team leader from opponent’s attacks. Drafting is a well-known strategy on flat or descending sections and has been studied before in this context. However, there are no systematic and extensive studies in the scientific literature on the aerodynamic effect of uphill drafting. Some studies even suggested that for gradients above 7.2% the speeds drop to 17 km/h and the air resistance can be neglected. In this paper, uphill drafting is analyzed and quantified by means of drag reductions and power reductions obtained by computational fluid dynamics simulations validated with wind tunnel measurements. It is shown that even for gradients above 7.2%, drafting can yield substantial benefits. Drafting allows cyclists to save over 7% of power on a slope of 7.5% at a speed of 6 m/s. At a speed of 8 m/s, this reduction can exceed 16%. Sensitivity analyses indicate that significant power savings can be achieved, also with varying bicycle, cyclist, road and environmental characteristics.
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Cycling performance models are used to study rider and sport characteristics to better understand performance determinants and optimise competition outcomes. Performance requirements cover the demands of competition a cyclist may encounter, whilst rider attributes are physical, technical and psychological characteristics contributing to performance. Several current models of endurance-cycling enhance understanding of performance in road cycling and track endurance, relying on a supply and demand perspective. However, they have yet to be developed for sprint-cycling, with current athlete preparation, instead relying on measures of peak-power, speed and strength to assess performance and guide training. Peak-power models do not adequately explain the demands of actual competition in events over 15-60 s, let alone, in World-Championship sprint cycling events comprising several rounds to medal finals. Whilst there are no descriptive studies of track-sprint cycling events, we present data from physiological interventions using track cycling and repeated sprint exercise research in multiple sports, to elucidate the demands of performance requiring several maximal sprints over a competition. This review will show physiological and power meter data, illustrating the role of all energy pathways in sprint performance. This understanding highlights the need to focus on the capacity required for a given race and over an event, and therefore the recovery needed for each subsequent race, within and between races, and how optimal pacing can be used to enhance performance. We propose a shift in sprint-cyclist preparation away from training just for peak power, to a more comprehensive model of the actual event demands.
Purpose: To compare the physical demands and performance indicators of male professional cyclists of 2 different categories (Union Cycliste Internationale WorldTour [WT] and ProTeam [PT]) during a cycling grand tour. Methods: A WT team (n = 8, 31.4 [5.4] y) and a PT team (n = 7, 26.9 [3.3] y) that completed "La Vuelta 2020" volunteered to participate. Participants' power output (PO) was registered, and measures of physical demand and physiological performance (kilojoules spent, training stress score, time spent at different PO bands/zones, and mean maximal PO [MMP] for different exertion durations) were computed. Results: WT achieved a higher final individual position than PT (31 [interquartile range = 33] vs 71 [59], P = .004). WT cyclists showed higher mean PO and kilojoule values than their PT peers and spent more time at high-intensity PO values (>5.25 W·kg-1) and zones (91%-120% of individualized functional threshold power) (Ps < .05). Although no differences were found for MMP values in the overall analysis (P > .05), subanalyses revealed that the between-groups gap increased through the race, with WT cyclists reaching higher MMP values for ≥5-minute efforts in the second and third weeks (Ps < .05). Conclusions: Despite the multifactorial nature of cycling performance, WT cyclists spend more time at high intensities and show higher kilojoules and mean PO than their PT referents during a grand tour. Although the highest MMP values attained during the whole race might not differentiate between WT and PT cyclists, the former achieve higher MMP values as the race progresses.
The aim of this study was to analyze climbing performance across two editions of a professional multistage race, and assess the influence of climb category, prior workload, and intensity measures on climbing performance in U23 and professional cyclists. Nine U23 cyclists (age 20.8 ± 0.9 years) and 8 professional cyclists (28.1 ± 3.2 years) participated in this study. Data were divided into four types: overall race performance, climb category, climbing performance metrics (power output, ascent velocity, speed), and workload and intensity measures. Differences in performance metrics and workload and intensity measures between groups were investigated. Power output , ascent velocity, speed were higher in professionals than U23 cyclists for Cat 1 and Cat 2 (p ≤ 0.001-0.016). Workload and intensity measures (Work total , Work total •km-1 , Elevation gain , eTRIMP and eTRIMP•km-1) were higher in U23 compared to professionals (p = 0.002-0.014). Climbing performance metrics were significantly predicted by prior workload and intensity measures for Cat 1 and 2 (R 2 = 0.27-0.89, p ≤ 0.001-0.030) but not Cat 3. These findings reveal that climbing performance in professional road cycling is influenced by climb categorization as well as prior workload and intensity measures. Combined, these findings suggest that Cat 1 and 2 climbing performance could be predicted from workload and intensity measures.
Purpose: The aim of this study was to compare the power profile, internal and external workloads, and racing performance between U23 and professional cyclists and between varying rider types across 2 editions of a professional multistage race. Methods: Nine U23 cyclists from a Union Cycliste Internationale "Continental Team" (age 20.8 [0.9] y; body mass 71.2 [6.3] kg) and 8 professional cyclists (28.1 [3.2] y; 63.0 [4.6] kg) participated in this study. Rider types were defined as all-rounders, general classification (GC) riders, and domestiques. Data were collected during 2 editions of a 5-day professional multistage race and split into the following 4 categories: power profile, external and internal workloads, and race performance. Results: The professional group, including domestiques and GC riders, recorded higher relative power profile values after certain amounts of total work (1000-3000 kJ) than the U23 group or all-rounders (P ≤ .001-.049). No significant differences were found for external workload measures between U23 and professional cyclists, nor among rider types. Internal workloads were higher in U23 cyclists and all-rounders (P ≤ .001-.043) compared with professionals, domestiques, and GC riders, respectively. The power profile significantly predicted percentage general classification and Union Cycliste Internationale points (R2 = .90-.99), whereas external and internal workloads did not. Conclusion: These findings reveal that the power profile represents a practical tool to discriminate between professionals and U23 cyclists as well as rider types. The power profile after 1000 to 3000 kJ of total work could be used by practitioners to evaluate the readiness of U23 cyclists to move into the professional ranks, as well as differentiate between rider types.