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“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Note. This article will be published in a forthcoming issue of the
International Journal of Sports Physiology and Performance. The
article appears here in its accepted, peer-reviewed form, as it was
provided by the submitting author. It has not been copyedited,
proofread, or formatted by the publisher.
Section: Original Investigation
Article Title: Relationship Between Various Training Load Measures in Elite Cyclists during
Training, Road Races and Time Trials
Authors: Teun van Erp1, Carl Foster1,2 and Jos J. de Koning1,2
Affiliations: 1Department of Human Movement Sciences, Vrije Universiteit Amsterdam,
Amsterdam Movement Sciences, The Netherlands, 2University of Wisconsin – La Crosse,
Department of Exercise and Sport Science, La Crosse, USA.
Journal: International Journal of Sports Physiology and Performance
Acceptance Date: September 24 2018
©2018 Human Kinetics, Inc.
DOI: https://doi.org/10.1123/ijspp.2017-0722
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Submission type: Original Investigation
Article Title: Relationship Between Various Training Load Measures in Elite Cyclists during
Training, Road Races and Time Trials
Authors: Teun van Erp1, Carl Foster1,2 and Jos J. de Koning1,2
Affiliations: 1Department of Human Movement Sciences, Vrije Universiteit Amsterdam,
Amsterdam Movement Sciences, The Netherlands, 2University of Wisconsin – La Crosse,
Department of Exercise and Sport Science, La Crosse, USA
Corresponding Author: Jos J. de Koning: j.j.de.koning@vu.nl
Running Head: Training load in cycling
Abstract word count: 250
Text only word count: 4218
Number of figures and tables: 1 figures, 7 tables
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Abstract
Purpose: The relationship between various training load (TL) measures in professional cycling
is not well explored. This study investigates the relationship between mechanical energy
spent (in kJ), sRPE, LuTRIMP and TSS in training, races and time trials (TT). Methods: From 4
consecutive years field data was collected from 21 professional cyclists and categorized as
being collected in training, racing or TT’s. kJ spent, sRPE, LuTRIMP and TSS were calculated
and the correlations between the various TL’s were made. Results: 11,655 sessions were
collected from which 7,596 sessions had heart rate (HR) data and 5,445 sessions had an RPE-
score available. The r between the various TL’s during training was almost perfect. The r
between the various TL’s during racing was almost perfect or very large. The r between the
various TL’s during TT’s was almost perfect or very large. For all relationships between TSS
and one of the other measurements of TL (kJ spent, sRPE and LuTRIMP) a significant different
slope was found. Conclusion: kJ spent, sRPE, LuTRIMP and TSS have all a large or almost
perfect relationship with each other during training, racing and TT’s but during racing both
sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Further, the significant
different slope of TSS versus the other measurements of TL during training and racing has the
effect that TSS collected in training and road-races differ by 120% while the other
measurements of TL (kJ spent, sRPE and LuTRIMP) differ by only 73%, 67%, and 68%
respectively).
Keywords: cycling, training monitoring, RPE, sRPE, LuTRIMP, TSS, high performance
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Introduction
Training load (TL) is one of the most important parameters for sport scientists and
coaches to monitor in elite athletes. TL needs to be high enough to induce a stimulus for
adaptation, with evidence that the magnitude of performance adaptation is proportional to
the training load.1 However, too high values for chronic TL are associated with overtraining
syndrome and other evidence of mal-adaptation to training.2 Large increases in TL are also
associated with injuries.3 It seems necessary to increase TL slowly for optimal improvement
in elite athletes.1 Further, to achieve a peak performance at a specific moment, there needs
to be an appropriate balance between the increase in fitness and the accumulation of fatigue
related to training.4,5 This balance is addressed in the rich literature on tapering.6
Training programs are mostly based on measurements of external load, which is the
TL independent of the individual response to training of the athlete. Coaches can prescribe a
training session based on sport specific parameters e.g. power output (PO), distance
completed, amount of throws or duration. External TL is based on the work completed
independently of the physiological or perceptual response within the individual athlete.
However, the relative physiological stress imposed on the athlete, the internal TL, is a more
important determinant of the stimulus for training adaptation than the external TL.7 Training
programs based on measurements of internal TL have mostly used heart rate (HR) or the
Rating of Perceived Exertion (RPE) to quantify TL.
HR is frequently used to determine TL because the technology is widely available, non-
invasive and inexpensive. The use of HR monitoring during exercise is based on the nearly
linear relationship between HR and oxygen consumption during steady-state submaximal
exercise.8 There are different means of calculating the TL using HR. Banister et al. 9 introduced
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
the concept of the TRIMP as a marker of TL based on the intensity of the exercise as calculated
by the product of the average %HRreserve and the duration of exercise. Multiple studies have
validated TL based on HR by correlating TL with other measurements of TL or with one of the
variations developed for TRIMP.10 One of the variations of TRIMP is LuTRIMP, which has been
widely used in professional road cycling.11-13 LuTRIMP is a summated score based on the
duration spent in each of the three HR zones, weighted for the intensity of the HR zone.
Although TL based on HR is frequently used in different sports, many factors may influence
the relationship between TL and HR.14 The day-to-day variation in HR is approximately ~6
beats/min 15 or < 6.5% 16 when factors influencing HR such as hydration, environment and
fatigue are not controlled.14 This can make measuring internal TL based on HR challenging in
the field. Further, the TRIMP method relies on the availability of known values of resting and
maximal HR.
Foster et al.17 introduced a non-invasive, inexpensive and easy to use method to
determine internal TL, based on a simple modification of the well-regarded RPE scale.18 The
TL of the exercise session is defined by multiplying the RPE of the entire session by the
duration of the session in minutes; the session RPE (sRPE). This method made the
measurement of TL independent of equipment. sRPE has been shown to be a valid and
reliable measure of TL19 and has a good correlation with TRIMP in elite cyclists during different
stages races.20 sRPE is minimally influenced by the time of measuring after training21, but is
influenced by environmental conditions22 and hydration status.23 Therefore the
measurement of TL by the use of sRPE can be challenging in the field as well.
In 1986 Ulrich Schoberer develop the first power meter (SRM) for the outdoor bicycle,
bringing a new power-based training approach to elite cycling. Coggan and Hunter developed
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
an approach to estimate TL based on continuous collection of the PO of a cyclist using a power
meter normalized using an individual threshold parameter the Functional Threshold Power
(FTP), called TrainingsStressScore (TSS).24 A workout of one hour on FTP represents a TSS-
score of 100 AU.
Sanders et al.25 investigated the effect of fatigue on the relationship between various
TL’s in elite cyclists. However, to the best of our knowledge, no study has investigated the
effect of different situations on the relationship between various TL’s. Therefore the aim of
this study was to investigate the relationship between various TL measures based on HR, RPE
and PO during training, road racing, and time-trials in a group of elite cyclists, across multiple
seasons. We hypothesized that the relationship between TL’s would be highest during
training and would be lower during road races and time trials, because, as mentioned above,
factors that influence internal TL are harder to control in competition.
Methods
Design
During 4 consecutive years we collected field data within a professional cycling team
(1 year Pro-continental and 3 years World Tour) with the aim of monitoring the cyclists and
analyzing their performance. Depending on the athlete’s tenure on the team, the data set of
an individual cyclist contains data ranging from 1 to 4 years. If a cyclist was not able to train
for a period of 3 months or more, because of illness or an injury, the data set of this particular
year was excluded. Institutional ethics approval was granted and, in agreement with the
Helsinki Declaration, written informed consent was obtained from the participants.
From as many as possible cycling sessions HR, RPE and PO were collected and
uploaded by the cyclists to a central database. All data was categorized as being collected in
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
training, road racing or time trials according to the UCI, based on www.procyclingstats.com.
The power-files categorized as racing or time trials could contain data from warm-up, cool-
down or reconnaissance and therefore may be longer than the actual race or time-trial. All
data sets were visually checked and incomplete data sets were excluded.
Subjects
Twenty-one highly trained professional cyclists participated in this study. During the 4
years of analysis the cyclists won combined, more than 100 UCI races, of which 45 victories
were in the World Tour, including 29 grand tour stages. Two cyclists also finished in the top
10 of the general classification of a grand tour. All riders had experience racing on World Tour
level and trained or raced ~ 30,000-35,000 km per year. Their average 20 minute mean
maximal power (MMP) of 413±32 W or 5.55±0.8 W/kg can be seen as a measure of their
excellence.
kJ spent
For every training, road race or time trial the total mechanical energy spent (in kJ) was
calculated from the PO as measure of external TL. PO was collected with the use of SRM
power-meters (SRM, Jülich, Welldorf, Germany) and Pioneer power meters (Pioneer,
Kawasaki, Japan). All riders were informed about the importance of the zero-calibration and
were instructed to do the zero-calibration before every ride, however this could not
controlled.
sRPE
All riders were obligated by the team to fill in their training logbook daily, as soon as
possible after the race or training. The RPE was obtained on a 6-20 scale 26,27 based on the
question in the logbook: ‘How hard was your day?’.18 The sRPE was calculated by multiplying
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
the RPE of the session by the duration of the exercise in minutes (equation 1) according to
Foster et al.1,17,19,28 as a measure of internal TL based on RPE. As a general rule, the total
time of the training session or race including warm-up and cool-down was included in the
duration element of sRPE.
(Equation 1)
The duration of the exercise was based on the collected data of that particular day.
When the power meter recorded speed, the duration of the exercise was based on the time
that speed was recorded. When no speed was recorded the duration of the session was based
on the time that HR was recorded. In the case that both speed and HR were missing, the
duration of the exercise was manually determined. The session was excluded from this study
when it was not possible to measure exercise duration.
LuTRIMP
Lucia’s TRIMP (LuTRIMP) 12,13 was used to calculate the internal TL based on HR. HR
was recorded with different brands of heart rate monitors (Suunto, Vantaa, Finland &
Pioneer, Kawasaki, Japan). LuTRIMP was calculated by multiplying the time spent in 3 HR-
zones (zone 1: below VT, zone 2: between VT and RCT and zone 3: above RCT) by a coefficient
relative to each intensity zone (k=1 for zone 1, k=2 for zone 2, k=3 for zone 3) according to
equation 2.
Equation 2)
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Originally the three HR-zones where LuTRIMP is based on, are computed based on
responses during laboratory exercise testing. However no laboratory exercise testing was
implemented within the team. Therefore we determined the HR-zones by the highest HR
recorded during every season (from November untill October) and used HR-zones as
described by Seiler.29,30 All HR data were manually checked for errors and when necessary
rejected from analysis.
TSS
TSS 24 was used as measure for internal TL based on PO. For every ride TSS was
calculated according to equation 3.
(Equation 3)
Were t is the duration of the session in seconds and IF the intensity factor (see
equation 5). NP is the normalized power as calculated with equation 4, where Pi is the floating
mean power during 30 seconds time segment and N is the total number of time segments.
Session duration t is obtained based on speed, HR or manually as described above. The
Functional Threshold Power (FTP) was determined as 95% of the highest 20 minutes MMP
obtained in every season.24
(Equation 4)
(Equation 5)
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Statistical analysis
Descriptive data are reported as means (standard deviation). MATLAB and Statistics
Toolbox (Release 2012b, The MathWorks, Inc., Natick, Massachusetts, United States) were
used to analyze the data and for the statistics. Differences between the four data sets used
to measure TL were determined by using N-way of analysis of variance (ANOVAN). When 10
or more training, races or time trials sessions were collected for an individual cyclist, the
relationship between all various TL’s was determined by using the Pearson correlation
coefficient. Regression lines are forced through zero, correlation coefficient (r and slopes)
values are described and uncertainties in the correlation coefficients are presented as 95% CI.
Further ANOVAN was used to determine significant difference between the slopes of the
relationships during training, racing or TT’s. As recommended by Hopkins, Fisher’s z-
transformation was used to transform correlation coefficients (r) before averaging and
calculating 95% confidence intervals (CI).31 The following criteria were adopted to interpret
the magnitude of the correlation (r) between the measures: < 0.1 trivial, 0.1-0.3 small, 0.3-
0.5 moderate, 0.5-0.7 large, 0.7-0.9 very large, and 0.9-1.0 almost perfect. The level of
statistical significance was set at 0.05.
Results
PO from 11,655 sessions were collected from which 7,596 sessions had HR data
available and 5,445 sessions had an RPE score. Based on the combined presence of RPE, HR
and PO the kJ spent, sRPE, LuTRIMP and TSS were calculated as measures of TL and are
presented together in Table 1 for all data combined and in Table 2, 3 and 4 for the training
sessions, road races and TT’s respectively.
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Relationship between various TL’s.
For the training sessions almost perfect correlations were found between all
relationships analyzed regardless of where the TL was based on (Table 5-10). For road racing
the relationship between kJ spent vs TSS and sRPE vs kJ spent were found to be almost perfect
where all other relationships in races are very large. For the TT’s an almost perfect correlation
was found between LuTRIMP vs kJ spent, TSS vs kJ spent and TSS vs LuTRIMP, where a very
large relationship was found between sRPE vs kJ spent, TSS vs sRPE and sRPE vs LuTRIMP.
Slopes did significantly differ between training vs races and training vs TT’s for all
relationship were TSS was involved (TSS vs kJ spent, TSS vs sRPE and TSS vs LuTRIMP). Where
slopes did not differ between training, race and TT’s session for the relationships based on
the other TL’s (sRPE vs kJ spent, LuTRIMP vs kJ spent and sRPE vs LuTRIMP). See figure 1 for
an example of a data set of one rider.
Discussion
This is the first study that has analyzed internal and external TL based on sRPE, HR and
PO in professional cycling, for multiple riders, across multiple seasons, in three different types
of cycling exercise. Some studies have presented internal TL based on HR (LuTRIMP) and RPE
data (sRPE) during grand tours 13,32 or stage races of between 5 and 7 days in durations 20 and
TT’s 33 but none have reported LuTRIMP and sRPE during training. Further TSS has only been
investigated during training in well-trained cyclists 11 and never during road races. This study
presents various measures of TL (kJ spent, sRPE, LuTRIMP and TSS) during training,
competition and TT’s in professional cycling in relation with each other. All four measures of
TL have very large or almost perfect correlations with each other measured in training, road
racing or time trials. However during road racing the correlation seems to be smaller when
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
the relationship is based on either LuTRIMP or sRPE, whereas the correlation with kJ spent
and TSS for competition stays within the 95% confidence intervals.
Another finding of this study is that the slopes between the relationships of various
TL’s (sRPE, LuTRIMP and kJ spent) did not differ between measurements collected during
training, racing or TT’s. While the slopes differ between training vs racing and training vs TT’s
when the relationship was based on TSS.
In general all relationships between various TL measures reported very large or almost
perfect relationships with each other, this could be caused by the fact that all measurements
are based on time. So despite that TL’s are also based on measures of intensity, RPE, HR and
PO, they all have time as a common parameter. The sessions used in this study are from highly
trained endurance athletes and therefore it could be possible that the long duration of the
sessions overwhelms the effect of RPE, HR and PO in the measurement of TL.
We expected that TSS would have an almost perfect correlation with kJ spent during
training, road racing and time trials because TSS is calculated with the use PO and duration
which is also the case for kJ spent. The biggest difference between TSS and kJ spent is that
FTP is used to normalize for differences in absolute PO between different cyclists. In this study
we determined the FTP for each cyclist by using the 20-minute MMP of the particular season.
Nimmerichter et al.34 showed that 20 minute MMP is changing by 0.4 W/kg during the season
and therefore that taking the highest 20 minute MMP to calculate FTP for an entire season
can cause an overestimation of TSS in the build-up periods of the year compared to the part
of the season when the highest 20 minute MMP was measured.
We observed a significant shorter average training duration for the TSS observations
(Table 2) and therefore the LuTRIMP and sRPE reported for training could be slightly
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
overestimated compared to TSS. The shorter duration of the TSS observations found its origin
in that in practice the importance of always recording HR and RPE during shorter recovery
rides is seen as low, while PO was automatically recorded. This could give a slightly
overrepresentation of these shorter rides in the TSS data.
For all the relationships where TSS is involved we found a significantly different slope
between training & racing and training & TT’s. Therefore the collection of TSS during training
versus race and TT’s is different compared to the other measurement of TL (kJ spent, LuTRIMP
and sRPE). This is clearly evident when looking to the difference of the average TL during
training and racing. When TL is based on TSS, on average the TL in races was 120% higher
compared to training. This increase in TL is larger than the increase in TL of the other
measurements, which is 73%, 67%, and 68% for kJ spent, LuTRIMP and sRPE respectively.
Around 10% of this difference can be explained by the significantly lower duration and PO
(table 2) measured in the TSS sessions. It seems likely that despite the good relationship
between TSS and various TL’s, there is a difference between TSS in road races and training,
probably caused by the importance of IF in the calculations of TSS. This should be clarified in
further analysis.
In line with others, we reported high correlations between various measurements of
TL and LuTRIMP.36 We found an almost perfect relationship between LuTRIMP vs kJ spent,
LuTRIMP vs TSS and LuTRIMP vs sRPE during training. However the present results reveal a
weaker relationship in road races between LuTRIMP vs kJ spent, LuTRIMP vs TSS and LuTRIMP
vs sRPE compared to the relationship in training. This was expected because during racing the
factors influencing HR are harder to control. Multiple factors could result in a lower or higher
HR compared to PO or RPE in races, with the result a weaker relationship with kJ spent and
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
TSS. It could be that fatigue plays a major role in the slightly weaker relationship, because in
professional cycling over 75% of the races are stage-races. This means that cyclists are racing
for multiple (up to 10) days without recovery days, with an increase in kJ spent of 73%
compared to training (Table 2-3). Halson 37 showed a reduction of 9.3% in maximum HR after
1 week of intensified training with limited recovery. Further Lucia 13 found an average decline
of maximum HR of 0.389/day during the Vuelta and 0.351/day during the Tour de France,
which was confirmed by Rodriguez-Marroyo.38 Because the HR-zones to calculate LuTRIMP
are not modified for this suppression in HR, the LuTRIMP scores are reduced in relation to the
external TL during multiple-day races. Thus, LuTRIMP could provide trainers and cyclists with
non-accurate TL in periods with a continuous high volume of external TL. Recently, Rodriguez-
Marroyo et al. 39 have recalculated the TRIMP, based on exercise tests performed immediately
before and immediately after grand tours. They have shown that if the anchors for the zones
in the TRIMP calculation are adjusted for the declining maximum HR during the course of a
grand tour, the percentage of time within various zones remains relatively constant from
week to week. Thus by taking the suppression of HR into account when data are collected
during a multiple stage race, a better estimation of internal TL on the basis of HR can be made.
A higher HR compared to PO could occur when riders are well-rested after a taper
period before the most important races and this has the opposite effect of fatigue on the HR-
PO relationship. Further, obtaining nutrition or fluids in a timely way during races is more
difficult compared to training. Because race stress makes timing harder and the availability of
nutrition or fluids during races is limited to supplies from the car or feeding zone. A limited
availability of nutrition and fluids could result in higher cardiac drift.40 Furthermore, riding in
a hot environment results in a higher cardiac stress40. Races start at a fixed schedule,
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
therefore it is impossible to avoid the hottest times of the day, while during training riders
can select their own timeslot to train and avoid heat stress. All mentioned factors will result
in a higher HR compared to PO and will therefore result in a higher LuTRIMP compared to kJ
spent or TSS.
We found similar values for LuTRIMP during road racing (356 AU) compared to the
values reported by Lucia (2003) 13 during the Tour de France (375 AU) and Vuelta a Espana
(362 AU). Despite the fact that we used maximum HR to determine the HR-zones instead of a
laboratory incremental cycling test, our values for LuTRIMP seems to be comparable to
previous studies.13
Despite many factors that can influence sRPE, such as hydration status 23 and
environmental conditions 22, we found an almost perfect relationship between sRPE and
various TL’s during training and a very large relationship during road races where sRPE vs kJ
spent was almost perfect. The slightly lower correlation between sRPE and various TL’s
obtained during road races can be caused by the factors mentioned above, because they can
be less controlled during races. Further, professional cyclists have a periodization schedule to
be in the best physiological condition at the start of important road races. Therefore it could
be that their physiological condition influences the relationship between sRPE and the other
measurements of TL. Furthermore, stage races can take up to 10 days of racing without a rest-
day and it could be possible that accumulating physical or mental fatigue makes this
relationship weaker during road races. However, there is still a very large relationship
between sRPE and other measures of TL during road races. Thus, sRPE seems to be a reliable
and practical tool to measure TL. Because the 6-20 “classical” version of the RPE scale seems
to translate for European athletes better than the 0-10 Category Ratio version of the scale,
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
we used the classical version, rather than the Category Ratio version used by Foster et
al.1,17,19,21,28 However, recent evidence from our laboratory41 suggests that the pattern of
response of the two versions of the RPE scale are very consistent, suggesting that, although
leading to numerically distinct sRPE values, the overall impression of the training session is
unaffected by the version of RPE scale used.
The results of our study indeed showed smaller correlations during races and time
trials for TL’s based on internal measurements (RPE and HR). For coaching these
uncontrollable influences on internal TL (LuTRMP & sRPE) are of great interest and for this
reason it would be discussable if a perfect correlation is desirable as it means that
physiological or mental fatigue are not measured and the additional value of measuring
internal TL is absent.
The reported results are from an observational study over multiple years with 21
professional cyclists. The presented data was primarily collected for monitoring professional
cyclists and secondly for the purpose of this study. Therefore the collected data has some
limitations. The measurement of PO was done with 2 different brands of power-meters.
Riders had different bicycles equipped with different power-meters and the riders had the
responsibility to calibrate the power-meters every day. To our knowledge, a systematic
comparison of different power-meters has not been reported in the literature. Further the
riders were obligated by the team to provide RPE-scores, but we did not control the timing of
obtaining the RPE-scores. Therefore it could be that RPE is recorded later than 30 min after
the exercise as recommend by Foster 1,17,28, because of unforeseen issues (logistics, bad
internet connection, press, podium ceremonies) or forgotten and filled in later. However,
recent data have shown that the sRPE is remarkably robust relative to the timing between
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
the exercise bout and the collection of the sRPE score.21 In the studied subjects, some did
strength training adjacent to cycling training, so the RPE-score can be influenced by the
strength training. Also HR was measured with different heart rate monitors, although
meaningful differences between brands of radio-telemetric heart rate belts have not been
reported.
Conclusion
TL’s based on RPE (sRPE), HR (LuTRIMP) and PO (kJ spent & TSS) are all reliable
measurements for measuring TL in training, road racing and TT’s in professional cyclists.
However, the difference between TSS collected in training and road-races (120%) is
unexpectedly higher compared to this difference in the other measures in TL (kJ spent, sRPE
and LuTRIMP, respectively 73%, 67%, and 68%). This is cause by the significantly different
slopes of TSS vs the other TL’s during training, races and TT’s. In further research the behavior
of LuTRIMP, sRPE and TSS during road races needs to be investigated to better understand
the relationship with external TL.
Application to practice
sRPE and LuTRIMP are methods for measuring internal TL which are simple and low
cost compared to TSS and can easily be used when there are no possibilities to train or
complete races with a power-meter. However, during races both methods to estimate
internal TL have a weaker relationship with kJ spent than the TSS and may therefore be less
reliable in comparison with TSS. On the other hand, despite the good correlation with kJ
spent, TSS shows unexpected higher values during racing compared to the increase of the
other measurements of TL. At the same time, the lack of a true “gold standard” for evaluating
the TL, particularly the internal TL, which may respond to progressive fatigue42,43, makes the
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
small differences observed within the current data perhaps less than critical. It suggests that
any method of monitoring TL, which is consistently applied and discussed between coach and
athlete, maybe more or less equivalent in net value.
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
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International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
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International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
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International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Figure 1: Relationships between various training load measures (kJ spent, LuTRIMP, sRPE and TSS) for
a representative subject collected during training (A), road races (B) and time trials (C). Each dot
represents one training or one competitive event.
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Table 1: Number of observations (N) and averages (SD) for session duration (time), power output
(power), external TL (kJ spent) and TL for RPE (sRPE), HR (LuTRIMP) and power output (TSS) measures
of TL of all exercise sessions. *significantly different from the three other data sets.
All Data
N
time (min)
power (W)
TL (AU)
kJ spent
11655
207
200
2572
sRPE
5455
211
198*
2889
LuTRIMP
7596
216 *
201
253
TSS
11655
207
200
153
Table 2: Number of observations with percentage of total observations (N) and averages (SD) for
session duration (time), power output (power), external TL (kJ spent) and TL for RPE (sRPE), HR
(LuTRIMP) and power output (TSS) measures of TL of the training sessions. *Significantly different
from the two other data sets. ** Significant different from LuTRIMP.
Training
N
time (min)
power (W)
TL (AU)
kJ spent
7867 (67%)
180* (98)
190** (36)
2137 (1261)
sRPE
4084 (75%)
194 (96)
192 (32)
2476 (1460)
LuTRIMP
5400 (71 %)
194 (97)
193 (29)
212 (113)
TSS
7867 (67%)
180* (98)
190** (36)
114 (77)
Table 3: Number of observations with percentage of total observations (N) and averages (SD) for
session duration (time), power output (power), external TL(kJ spent) and TL for RPE (sRPE), HR
(LuTRIMP) and power output (TSS) measures of TL of the road races.
Road Races
N
time (min)
power (W)
TL (AU)
kJ spent
3457 (30%)
281 (61)
218 (36)
3701 (988)
sRPE
1241 (23%)
279 (62)
217 (33)
4137 (1217)
LuTRIMP
2056 (27%)
281 (61)
219 (33)
356 (92)
TSS
3457 (30%)
281 (61)
218 (36)
251 (72)
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Table 4: Number of observations with percentage of total observations (N) and averages (SD) for
session duration (time), power output (power), external TL (kJ spent) and internal TL for RPE (sRPE),
HR (LuTRIMP) and power output (TSS) measures of TL of the time trials
Time Trials
N
time (min)
power (W)
TL (AU)
kJ spent
331 (3%)
81 (51)
249 (87)
1411 (659)
sRPE
120 (2%)
85 (48)
235 (77)
1298 (723)
LuTRIMP
140 (2%)
80 (52)
255 (78)
116 (65)
TSS
331 (3%)
81 (51)
249 (87)
82 (45)
Table 5: Average correlation coefficient and the regression coefficient (slope) between sRPE and kJ
spent for training sessions, road racing and time trials presented with 95% confidence intervals.
sRPE versus kJ spent
N cyclists
N datasets
r [ 95% CI]
Slope [ 95% CI]
Training
21
4084
0.97 [0.96 to 0.97]
0.90 [0.87 to 0.94]
Road racing
19
1241
0.91 [0.89 to 0.94]
0.85 [0.80 to 0.89]
Time Trials
4
60
0.89 [0.67 to 0.97]
0.90 [0.74 to 1.06]
Table 6: Average correlation coefficient and the regression coefficient (slope) between LuTRIMP and
kJ spent for training sessions, road racing and time trials presented with 95% confidence intervals.
LuTRIMP versus kJ spent
N cyclists
N datasets
r [ 95% CI]
Slope [ 95% CI]
Training
21
4084
0.97 [0.96 to 0.97]
10.8 [10.5 to 11.1]
Road racing
19
1241
0.85 [0.82 to 0.87]
10.1 [9.6 to 10.7]
Time Trials
4
60
0.93 [0.90 to 0.94]
9.8 [ 9.0 to 10.5]
Table 7: Average correlation coefficient and the regression coefficient (slope) between TSS and kJ
spent for training sessions, road racing and time trials presented with 95% confidence intervals.
*significantly different from the two other data sets.
TSS versus kJ spent
N cyclists
N datasets
r [ 95% CI]
Slope [ 95% CI]
Training
21
4084
0.96 [0.95 to 0.96]
17.9 [ 17.2 to 18.7]*
Road racing
19
1241
0.94 [0.94 to 0.95]
14.7 [14.1 to 15.3]
Time Trials
4
60
0.98 [0.97 to 0.99]
13.7 [13.2 to 14.3]
International Journal of Sports Physiology and Performance
“Relationship Between Various Training Load Measures in Elite Cyclists during Training, Road Races and Time Trials”
by van Erp T, Foster C, de Koning JJ
International Journal of Sports Physiology and Performance
© 2018 Human Kinetics, Inc.
Table 8: Average correlation coefficient and the regression coefficient (slope) between TSS and sRPE
for training sessions, road racing and time trials presented with 95% confidence intervals.
*significantly different from the two other data sets.
TSS versus sRPE
N cyclists
N datasets
r [ 95% CI]
Slope [ 95% CI]
Training
21
4084
0.95 [0.95 to 0.96]
20.3 [19.3 to 21.3]*
Road racing
19
1241
0.86 [0.82 to 0.89]
17.4 [16.6 to 18.3]
Time Trials
4
60
0.86 [0.57 to 0.96]
15.3 [13.8 to 16.7]
Table 9: Average correlation coefficient and the regression coefficient (slope) between TSS and
LuTRIMP for training sessions, road racing and time trials presented with 95% confidence intervals.
*significantly different from the two other data sets.
TSS versus LuTRIMP
N cyclists
N datasets
r [ 95% CI]
Slope [ 95% CI]
Training
21
5400
0.95[0.95 to 0.96]
1.64 [1.59 to 1.69]*
Road racing
18
774
0.82 [0.80 to 0.85]
1.42 [1.35 to 1.48]
Time Trials
5
77
0.93 [0.91 to 0.95]
1.37 [1.28 to 1.45]
Table 10: Average correlation coefficient and the regression coefficient (slope) between sRPE and
LuTRIMP spent for training sessions, road racing and time trials presented with 95% confidence
intervals. * Sample size is too small for statistical testing.
sRPE versus LuTRIMP
N cyclists
N datasets
r [ 95% CI]
Slope [ 95% CI]
Training
21
2950
0.97 [0.96 to 0.98]
0.083 [0.079 to 0.086]
Road racing
13
774
0.88 [0.85 to 0.91]
0.084 [0.078 to 0.090]
Time Trials
1
15
0.85*
0.092 *
International Journal of Sports Physiology and Performance