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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. Conclusions:: 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).
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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 athletes 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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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)
7867 (67%)
180* (98)
190** (36)
2137 (1261)
4084 (75%)
194 (96)
192 (32)
2476 (1460)
5400 (71 %)
194 (97)
193 (29)
212 (113)
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)
3457 (30%)
281 (61)
218 (36)
3701 (988)
1241 (23%)
279 (62)
217 (33)
4137 (1217)
2056 (27%)
281 (61)
219 (33)
356 (92)
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)
331 (3%)
81 (51)
249 (87)
1411 (659)
120 (2%)
85 (48)
235 (77)
1298 (723)
140 (2%)
80 (52)
255 (78)
116 (65)
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
... At a more mechanistic level, reductions in muscle force or power generating ability have been attributed to a decline in muscle contractile function (i.e., "peripheral fatigue"), and/or a reduction in central motor output (i.e., "central fatigue") (Gandevia 2001). During cycling exercise, for example, peripheral aspects of performance fatigability become exacerbated in an intensity-dependent manner, whereas reductions in central motor output become more pronounced as exercise duration increases (Thomas et al. 2016;Iannetta et al. 2022). In addition to neuromuscular mechanisms, robust evidence indicates that the limit of endurance performance is intricately modulated by perceptual factors such as pain and effort (Graven-Nielsen et al. 2002;Amann et al. 2013;Pageaux et al. 2015;Aboodarda et al. 2020). ...
... Due to these complexities, and as inferred by prior studies (Kesisoglou et al. 2020;Fullerton et al. 2021;Vermeire et al. 2023), any association between TRIMP and performance fatigability following an exercise session may be weak and context-dependent. As highlighted by Passfield et al. (2022), this discrepancy has important implications, considering that metrics such as TWD are still commonly used to monitor the training and performance potential of athletes (Halson 2014;van Erp et al. 2019), as well as to standardize training interventions of different intensities and durations (Stewart et al. 2016;. Importantly, while these prior studies certainly question the general notion that performance fatigability is proportional to the TRIMP (as estimated using TWD or HR-based calculations), they only assessed performance fatigability via TTF or time trial performance. ...
... One benefit of performing interval exercise is that the interspersed recovery periods allow some restoration of the metabolic state within the muscle fibers so that the exercise may be prolonged (and thus, more work can be performed) (Chidnok et al. 2013a). Still, peripheral-mediated mechanisms predominantly limit high-intensity exercise performance (Jones et al. 2008;Black et al. 2017;Burnley and Jones 2018), whereas at lower intensities for which muscle metabolic perturbations can reach a steady state (Jones et al. 2008), central factors may become more important (Thomas et al. 2016;Burnley and Jones 2018;Iannetta et al. 2022). In the present study, considering that there were no significant differences observed between PRE and POST EX for indices of central fatigue, the lack of correlation between changes in these measures and changes in cycling performance was expected. ...
Article
Full-text available
Classical training theory postulates that performance fatigability following a training session should be proportional to the total work done (TWD); however, this notion has been questioned. This study investigated indices of performance and perceived fatigability after primary sessions of high-intensity interval training (HIIT) and constant work rate (CWR) cycling, each followed by a cycling time-to-task failure (TTF) bout. On separate days, 16 participants completed an incremental cycling test, and, in a randomized order, (i) a TTF trial at 80% of peak power output (PPO), (ii) an HIIT session, and (iii) a CWR session, both of which were immediately followed by a TTF trial at 80% PPO. Central and peripheral aspects of performance fatigability were measured using interpolated twitch technique, and perceptual measures were assessed prior to and following the HIIT and CWR trials, and again following the TTF trial. Despite TWD being less following HIIT (P = 0.029), subsequent TTF trial was an average of 125 s shorter following HIIT versus CWR (P < 0.001), and this was accompanied by greater impairments in voluntary and electrically evoked forces (P < 0.001), as well as exacerbated perceptual measures (P < 0.001); however, there were no differences in any fatigue measure following the TTF trial (P ≥ 0.149). There were strong correlations between the decline in TTF and indices of peripheral (r = 0.70) and perceived fatigability (r ≥ 0.80) measured at the end of HIIT and CWR. These results underscore the dissociation between TWD and performance fatigability and highlight the importance of peripheral components of fatigability in limiting endurance performance during high-intensity cycling exercise.
... This is supported by the fact that the modalities of training (intensity) seem to have a larger effect on APD than the TWD, both in this study and in previous research using a similar approach. 7 This is especially noteworthy to mention, since the TWD is often used in studies and in the field to compare races, 23 TL methods, 22,24 or training programs. 25,26 Still, the same pattern was found for TWD and the total volume ofVO 2 during training sessions, suggesting that TWD andVO 2 are related to each other. ...
... It is important for practitioners to understand that using a different TL quantification method leads to differences in the load calculated, even when using the same input variable (HR for TRIMPs, power output for TSS and TWD). It is also important to understand that these differences in calculation may often be hidden in practice when looking at correlations between TL methods for a lot of data and much longer training sessions 10,22,24 ; still, this does not mean that these differences are to be overlooked. Also, we discourage the use of TWD as a basis for matching and comparing efforts between or within groups. ...
Article
Purpose: To examine the differences in training load (TL) metrics when quantifying training sessions differing in intensity and duration. The relationship between the TL metrics and the acute performance decrement measured immediately after the sessions was also assessed. Methods: Eleven male recreational cyclists performed 4 training sessions in a random order, immediately followed by a 3-km time trial (TT). Before this period, participants performed the time TT in order to obtain a baseline performance. The difference in the average power output for the TTs following the training sessions was then expressed relative to the best baseline performance. The training sessions were quantified using 7 different TL metrics, 4 using heart rate as input, 2 using power output, and 1 using the rating of perceived exertion. Results: The load of the sessions was estimated differently depending on the TL metrics used. Also, within the metrics using the same input (heart rate and power), differences were found. TL using the rating of perceived exertion was the only metric showing a response that was consistent with the acute performance decrements found for the different training sessions. The Training Stress Score and the individualized training impulse demonstrated similar patterns but overexpressed the intensity of the training sessions. The total work done resulted in an overrepresentation of the duration of training. Conclusion: TL metrics provide dissimilar results as to which training sessions have higher loads. The load based on TL using the rating of perceived exertion was the only one in line with the acute performance decrements found in this study.
... Furthermore, very large correlations (r = 0.96-0.97) have been reported across multiple measures of internal and external training load in cyclists during racing and training [15], suggesting other metrics such as session rating of perceived exertion (sRPE) [16], Lucia training impulse (LuTRIMP) [17], and training stress score (TSS) [18] can also provide relevant information to athletes and coaches. However, there is no gold standard measure of training load [13], and measures of external and internal load are not always consistent. ...
Article
Full-text available
Background Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions. Objectives We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load measures and easily quantified individual factors. Methods This study was undertaken in two parts: a primary study, where participants performed four different laboratory-based cycle training sessions, and a validation study where different participants performed a single laboratory-based training session using one of three exercise modalities (cycling, running, or kayaking). The primary study included 15 cyclists (five female; maximal oxygen consumption [V˙V˙\dot{V}O2max], 51.9 ± 7.2 mL/kg/min), the validation study included 21 cyclists (seven female; V˙V˙\dot{V}O2max 53.5 ± 11.0 mL/kg/min), 20 runners (six female; V˙V˙\dot{V}O2max 57.5 ± 7.2 mL/kg/min), and 18 kayakers (five female; V˙V˙\dot{V}O2max 45.6 ± 4.8 mL/kg/min). Training sessions were quantified using six training load metrics: two using heart rate, three using power, and one using perceived exertion. Carbohydrate use and EE were determined separately for aerobic (gas exchange) and anaerobic (net lactate accumulation, body mass, and O2 lactate equivalent method) energy systems and summed. Repeated-measures correlations were used to examine relationships between training load and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using training load alongside measures of fitness and sex. Models were built in the primary study and tested in the validation study. Model performance is reported as the coefficient of determination (R²) and mean absolute error, with measures of calibration used for model evaluation and for sport-specific model re-calibration. Results Very-large to near-perfect within-subject correlations (r = 0.76–0.96) were evident between all training load metrics and both CHO utilization and EE. In the primary study, all models explained a large amount of variance (R² = 0.77–0.96) and displayed good accuracy (mean absolute error; CHO = 16–21 g [10–14%], EE = 53–82 kcal [7–11%]). In the validation study, the mean absolute error ranged from 16–50 g [15–45%] for CHO models to 53–182 kcal [9–31%] for EE models. The calibrated mean absolute error ranged from 9–20 g [8–18%] for CHO models to 36–72 kcal [6–12%] for EE models. Conclusions At the individual level, there are strong linear relationships between all measures of training load and both CHO utilization and EE during cycling. When combined with other measures of fitness, EE and CHO utilization during cycling can be estimated accurately. These models can be applied in running and kayaking when used with a calibration adjustment.
... Furthermore, very large correlations (r = 0.96-0.97) have been reported across multiple measures of internal and external training load in cyclists during racing and training [15], suggesting other metrics such as session rating of perceived exertion (sRPE) [16], Lucia training impulse (LuTRIMP) [17], and training stress score (TSS) [18] can also provide relevant information to athletes and coaches. ...
... We argue that misinterpretations or misuses of training data can have significant negative consequences for runners, such as increased risk of injury, and better technology design could alleviate these problems [93]. Hence, TLM is not just a matter of optimizing performance in running but is also crucial for injury prevention in other sports, such as cycling [89], swimming [5], and team sports [59]. Understanding how runners manage their training load more profoundly could help design sports trackers to better guide TLM, and thus help avoid undesirable outcomes. ...
Conference Paper
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Training Load Management (TLM) is crucial for achieving optimal athletic performance and preventing chronic sports injuries. Current sports trackers provide runners with data to manage their training load. However, little is known about the extent and the way sports trackers are used for TLM. We conducted a survey (N=249) and interviews (N=24) with runners to understand sports tracker use in TLM practices. We found that runners possess some understanding of training load and generally trust their trackers to provide accurate training load-related data. Still, they hesitate to strictly follow trackers' suggestions in managing their training load, often relying on their intuitions and body signals to determine and adapt training plans. Our findings contribute to SportsHCI research by shedding light on how sports trackers are incorporated into TLM practices and providing implications for developing trackers that better support runners in managing their training load. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI.
... Furthermore, a significant difference was found in the psychological state of athletes between training and competition, with the former being more monotonous and the latter generating stronger emotional responses, leading to notable psychological differences. Differences in training and competition intensity can result in varying effects of load quantification [44]. The correlation between load monitoring methods and changes in physiological performance in this study, whilst moderate, is far less than has been found in practice in other projects. ...
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This study aimed to: (i) analyze the load characteristics of 4 weeks cross-country skiing altitude training; (ii) analyze the relationships between methods of monitoring training load and physiological indicators changes of elite male Chinese cross-country skiers during this period. Practitioners collected load data during 4 weeks of altitude training camp. Participants performed maximal oxygen uptake, lactate threshold, body composition, and skierg power test before and after the training camp to investigate the changes in physiological performance. Edwards TRIMP, Lucia TRIMP, and session rating of perceived exertion were collected as internal load. Training distance, time recorded by the Catapult module were collected as external load. The result revealed a " pyramid " pattern in the load characteristics during the altitude training camp. The correlation between luTRIMP and percent change in physiological indicators was highest. Percentage changes in lactate threshold velocity (r = .78 [95% CI -.01 to .98]), percentage changes in lactate threshold HR (r = .71 [95% CI .14- .99]), percentage changes in maximum HR (r = .83 [95% CI .19–1.00]), percentage changes in skierg power-to-weight ratio (r = .75 [95% CI -.28 to .98]) had very large relationships with luTRIMP. In cross-country skiing altitude training, training loads should be reasonably controlled to ensure that athletes do not become overly fatigued. Methods of training load monitoring that combine with athletes’ physiological characteristics and program characteristics have the highest dose-response relationships, it is an important aspect of cross-country ski training load monitoring. The luTRIMP could be a good monitoring tool in cross-country skiing altitude training.
... 3,4 With digital monitoring technologies, access to daily training data from elite road cyclists has greatly expanded in recent years. [5][6][7][8][9] These developments have also energized discussions about training periodization in cycling. ...
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Unlabelled: A well-planned periodized approach endeavors to allow road cyclists to achieve peak performance when their most important competitions are held. Purpose: To identify the main characteristics of periodization models and physiological parameters of trained road cyclists as described by discernable training intensity distribution (TID), volume, and periodization models. Methods: The electronic databases Scopus, PubMed, and Web of Science were searched using a comprehensive list of relevant terms. Studies that investigated the effect of the periodization of training in cyclists and described training load (volume, TID) and periodization details were included in the systematic review. Results: Seven studies met the inclusion criteria. Block periodization (characterized by employment of highly concentrated training workload phases) ranged between 1- and 8-week blocks of high-, medium-, or low-intensity training. Training volume ranged from 8.75 to 11.68 h·wk-1 and both pyramidal and polarized TID were used. Traditional periodization (characterized by a first period of high-volume/low-intensity training, before reducing volume and increasing the proportion of high-intensity training) was characterized by a cyclic progressive increase in training load, the training volume ranged from 7.5 to 10.76 h·wk-1, and pyramidal TID was used. Block periodization improved maximum oxygen uptake (VO2max), peak aerobic power, lactate, and ventilatory thresholds, while traditional periodization improved VO2max, peak aerobic power, and lactate thresholds. In addition, a day-by-day programming approach improved VO2max and ventilatory thresholds. Conclusions: No evidence is currently available favoring a specific periodization model during 8 to 12 weeks in trained road cyclists. However, few studies have examined seasonal impact of different periodization models in a systematic way.
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Purpose: Performance assessment and analysis of aerobic endurance athlete usually requires the use of expansive measurement equipment in a laboratory setting. The main motivation for the paper was the fulfilment of a goal of obtaining a reliable, objective and easily measurable endurance athlete’s performance parameter, which would serve coaches and athletes to track their progress, as well as a benchmark for comparison amongst the athletes competing in the same discipline. Methods: The main idea of the paper originates from the fact that each individual endurance workout causes a physiological response in a human body. Therefore, the newly developed athlete’s performance parameter - velocity quotient (VQ) considers both the athlete’s actual performance (velocity) and his/her response (heart rate) to it. The VQ parameter’s behavior was theoretically investigated in case of running. The reliability of the VQ parameter was also experimentally validated with dataset obtained during multiple running exercises of a single recreational level athlete. Results: The velocity quotient showed potential for being a reliable predictor of endurance runners’ performance on a theoretical base which was supported by the preliminary experimental validation study, which produced the value of Pearson's correlation coefficient between the velocity quotient and the sports watch estimated VO2max of 0.7942. Conclusion: The behavior of the velocity quotient was predicted at various exercise intensities (average heart rates) and fitness levels. Fairly good correlation between the velocity quotient and the sports watch estimated VO2max was found while more daily variations of VQ than for VO2max were observed.
Thesis
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Handcycling has become a popular recreational and competitive form of Para-cycling. Like their able-bodied cycling counterparts, competitive handcyclists continue to explore ways by which to gain a performance edge. Whilst our collective understanding as to the influence of handbike design and configuration, handcycling technique, and the physiological determinants of competitive handcycling has improved over the past 20-years, there still remains several gaps in our scientific knowledge as to the most effective approach by which to optimise handcycling performance capabilities. Therefore, the following PhD thesis summarises five thematically linked publications, and two related conference presentations which aimed to investigate the development and implementation of appropriate training interventions designed to enhance the performance of recumbent handcyclists across the spectrum of competitive events including road races, time-trials, and ultra-endurance challenges. Publication 1 (Chapter 3) represents a pilot research project which aimed to investigate the effectiveness of concurrent strength and endurance training on handcycling performance. Whilst demonstrating that concurrent training was more effective than endurance training alone this study generated several pertinent research questions. These included what are the physiological determinants of real-world handcycling performance? What is the relationship between upper-body strength and handcycling performance? and would a long-term concurrent training intervention elicit greater improvements in performance capabilities? To address these questions Publication 2 (Chapter 4) and Publication 3 (Chapter 5) build upon the published literature and identify the physiological determinants of handcycling performance. However, for the first time in the literature these studies also investigate the relationship between upper-body strength measures, anaerobic capacity and identified determinants of handcycling performance. Based upon these findings Publication 4 (Chapter 6) reports upon the effectiveness of a 30-week concurrent training program based upon a block periodisation model. Furthermore, this study reports the performance profile of an elite handcyclist during a 1407-km ultra-endurance handcycling challenge. Building upon this body of work, Publication 5 (Chapter 7) represents a holistic narrative review led by the author and written in conjunction with a group of international researchers in the field of handcycling. This piece aims to translate handcycling specific research and provide useful insights to riders, coaches and sports scientists as to the history of handcycling, functional classification levels, handbike configuration, the physiological determinants of handcycling performance, and the best approach by which to develop handcycling performance capabilities. In summary, the body of work presented within this PhD thesis has added to our collective knowledge in regard to understanding the physiological determinants of handcycling performance including the importance of quantifying anaerobic capacity and upper-body strength. Furthermore, from an applied perspective the work presented demonstrates that concurrent strength and endurance training based upon a block periodisation model appears to be an effective approach by which to develop both TT and ultra-endurance handcycling performance. Taken collectively this knowledge adds to the existing body of literature and will positively impact upon the ability of riders, coaches, and sport scientists to optimise recumbent handcycling performance capabilities. Future studies should aim to use classifiable handcyclists with the intent of translating their findings to the wider handcycling community with the goal of not only enhancing handcycling performance but also improving the functional capabilities of a valued but often under represented section of society.
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Objetivo: analisar a relação entre o perfil antropométrico e o desempenho em provas de ciclismo de estrada de atletas de elite do sexo feminino. Metodologia: Foram avaliadas 22 ciclistas divididas em dois grupos: ciclistas de nível nacional (GN; n = 12) e cliclistas de nível estadual (GE; n = 10). O desempenho das atletas do GN foi obtido no Campeonato Brasileiro e do GE nos Jogos Abertos do Paraná, ambos em 2019. A comparação das medidas antropométricas entre grupos foi calculada por meio do teste t two-tailed de Student não pareado. A relação entre as variáveis intragrupo foi analisada através do teste de correlação de Pearson. Resultados e Discussão: Foi identificada diferença significativa entre os grupos no valor da massa óssea (GN = 6,70 ± 0,67 kg; GE = 7,29 ± 0,53 kg; p = 0,036). Verificou-se também: correlação positiva e moderada entre percentual de gordura e desempenho esportivo no GN nas provas Estrada (r=0,33) e contrarrelógio individual (CRI) (r= 0,36); Correlação negativa e forte no GN entre idade e desempenho esportivo nas provas Estrada (r= -0,53) e CRI (r= -0,58); Correlação negativa e moderada no GN entre Massa Muscular e Desempenho esportivo na prova Estrada (r= -0,38); Correlação negativa e forte no GN entre Massa Muscular e Desempenho esportivo na prova CRI. Conclusão: Conclui-se que o desempenho esportivo das atletas de elite pode ser alcançado com perfis antropométricos e etários distintos, e evidenciam que o rendimento não depende exclusivamente de uma única variável.
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The Rating of Perceived Exertion (RPE) is an important measure of exercise intensity, which is useful both as a primary and adjunctive method of exercise prescription. However, there are multiple variants of the Borg RPE scale, primarily the Borg 6-20 RPE scale (BORG-RPE) and the Borg Category-Ratio-10 scale (BORG-CR10). There are inadequate data available to address the comparability and interchangeability of these two widely used scales. Well-trained non-athletes performed two increment cycle tests, with each scale used in a random sequence. Subjects also performed interval sessions at three intensities (50, 75 and 85% of peak power output) with each scale used in a random sequence. There were very large correlations during the incremental exercise between the conventional physiological measures (% heart rate reserve – r=0.89 & r=.87); and %VO2reserve (r=.88 & r=.90) and RPE measured by either the BORG-RPE or the BORGCR10, respectively. This pattern was also evident during the interval exercise (% heart rate reserve – r=.85 & r=.84; and blood lactate concentration – r=.74 & r=.78) and RPE measured by either the BORG-RPE or the BORG-CR10, respectively. The relationship between RPE measured by the BORG-RPE and the BORGCR10 was large and best described by a non-linear relationship for both the incremental (R2=.89) and the interval (R2=.89) exercise. The incremental and interval curves were virtually overlapping. We concluded that the two most popular versions of the RPE scale, BORG-RPE and BORG-CR10, were both highly related to the conventional physiological measures and very strongly related to each other, with an easily described conversion.
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This study evaluated the changes in ratios of different intensity (rating of perceived exertion; RPE, heart rate; HR, power output; PO) and load measures (session-RPE; sRPE, individualized TRIMP; iTRIMP, Training Stress Score™; TSS) in professional cyclists. RPE, PO and HR data was collected from twelve professional cyclists (VO2max 75 ± 6 ml∙min∙kg⁻¹) during a two-week baseline training period and during two cycling Grand Tours. Subjective:objective intensity (RPE:HR, RPE:PO) and load (sRPE:iTRIMP, sRPE:TSS) ratios and external:internal intensity (PO:HR) and load (TSS:iTRIMP) ratios were calculated for every session. Moderate to large increases in the RPE:HR, RPE:PO and sRPE:TSS ratios (d = 0.79–1.79) and small increases in the PO:HR and sRPE:iTRIMP ratio (d = 0.21–0.41) were observed during Grand Tours compared to baseline training data. Differences in the TSS:iTRIMP ratio were trivial to small (d = 0.03–0.27). Small to moderate week-to-week changes (d = 0.21–0.63) in the PO:HR, RPE:PO, RPE:HR, TSS:iTRIMP, sRPE:iTRIMP and sRPE:TSS were observed during the Grand Tour. Concluding, this study shows the value of using ratios of intensity and load measures in monitoring cyclists. Increases in ratios could reflect progressive fatigue that is not readily detected by changes in solitary intensity/load measures.
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PURPOSE: The aim of this study was to analyze professional cyclists' performance decline after, and the exercise demands during, a Grand Tour. METHOD: Seven professional cyclists performed two incremental exercise tests, 1-week before and the day after the Vuelta España. During the race the exercise demands were analyzed on the basis of the HR. Three intensity zones were established according to reference HR values corresponding to the ventilatory (VT) and respiratory compensation (RCT) thresholds determined during the pre-race test. In addition, exercise demands for the last weeks of the Vuelta were recalculated: using the reference HR determined during the post-race test for the 3rd week and averaging the change observed in the VT and RCT per stage for the 2nd week. The reference HR for the beginning of the 2nd week was estimated. RESULTS: A significant (P-value range, 0.044-0.000) decrement in VO2, power output and HR at maximal exercise, VT and RCT were found after the race. Based on the pre-race test, the mean time spent daily above the RCT was 13.8 ± 10.2 min. This time decreased -1.2 min·day-1 across the race. When the exercise intensity was corrected according to the post-race test, the time above RCT (34.1±9.9 min) increased 1.0 min·day-1. CONCLUSION: These data indicate that completing a Grand Tour may result in a significant decrement in maximal and submaximal endurance performance capacity. This may modify reference values used to analyze the exercise demands. As a consequence, the high-intensity exercise performed by cyclists may be underestimated.
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The purpose of this study was to compare physiological responses and perceived exertion among well trained cyclists (n=63) performing three different high intensity interval training (HIIT) prescriptions differing in work bout duration and accumulated duration, but all prescribed with maximal session effort. Data are presented as mean(Standard Deviation). Subjects (male, 38(8) y, VO2peak 62(6) mL.kg(-1) min(-1)) completed up to 24 HIIT sessions over 12 weeks as part of a training intervention study. Sessions were prescribed as 4x16 min, 4x8 min, or 4x4 min with 2 min recovery periods (8 sessions of each prescription, balanced over time). Power output, HR, and RPE were collected during and after each work bout. Session RPE was reported after each session. Blood lactate samples were collected throughout the 12 weeks. Physiological and perceptual responses during > 1400 training sessions were analyzed. HIIT sessions were performed at 95(5), 106(5), and 117(6)% of 40min TT power during 4x16, 4x8, and 4x4 min sessions respectively with peak HR in each work bout averaging 89(2), 91(2) and 94(2)% HRpeak. Blood lactate concentration was 4.7(1.6), 9.2(2.4) and 12.7(2.7) mMol.L(-1). Despite the common prescription of maximal session effort, RPE and sRPE increased with decreasing accumulated work duration (AWD), tracking relative HR. Only 8% of 4x16 min sessions reached RPE 19-20, versus 61% of 4x4 min sessions. We conclude that within the HIIT duration range, performing at "maximal session effort" over a reduced AWD is associated with higher perceived exertion both acutely and post exercise. This may have important implications for HIIT prescription choices.
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Purpose: The aim of this study was to assess the dose-response relationships between different training load methods and aerobic fitness and performance in competitive road cyclists. Method: Training data from 15 well-trained competitive cyclists were collected during a 10-week (December - March) pre-season training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister's TRIMP (bTRIMP), Edwards' TRIMP (eTRIMP), individualized TRIMP (iTRIMP), Lucia's TRIMP (luTRIMP) and session-RPE (sRPE). External load was measured using Training Stress Score™ (TSS). Results: Large to very large relationships (r = 0.54-0.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol·L(-1)) were observed for all training load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = 0.81 [95% CI: 0.51 to 0.93, r = 0.77 [95% CI 0.43 to 0.92]) and TSS (r = 0.75 [95% CI 0.31 to 0.93], r = 0.79 [95% CI: 0.40 to 0.94]). The highest dose-response relationships with changes in the 8MT performance test were observed for iTRIMP (r = 0.63 [95% CI 0.17 to 0.86]) and luTRIMP (r = 0.70 [95% CI: 0.29 to 0.89). Conclusions: The results show that training load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.
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Purpose: The session RPE (sRPE) has gained popularity as a "user friendly" method for evaluating internal training load. sRPE has historically been obtained 30-min following exercise. This study evaluated the effect of post-exercise measurement time on sRPE following steady-state and interval cycle exercise. Methods: Well-trained subjects (N=15) (VO2max=51+4 & 36+4 ml.kg-1 (cycle ergometer) for men & women, respectively) completed counterbalanced 30-min steady-state and interval training bouts. The steady-state ride was at 90% of ventilatory threshold (VT). The work-to-rest ratio of the interval rides was 1:1 and the interval segment durations were 1-, 2- & 3-min. The high-intensity component of each interval bout was 75% peak power output (PPO), which was accepted as a surrogate of the respiratory compensation threshold, critical power or maximal lactate steady state. Heart rate (HR), blood lactate [BLa], and Rating of Perceived Exertion (RPE) were measured. The sRPE (Category Ratio Scale) was measured at 5-, 10-, 15-, 20-, 2-, 30-, 60-min and 24-hr following each ride, using a Visual Analog Scale (VAS) to prevent bias associated with specific RPE verbal anchors. Results: sRPE, at 30-min post exercise, followed a similar trend: steady state=3.7, 1-min=3.9, 2-min=4.7, 3-min=6.2. No significant differences (p > 0.05) in sRPE were found based on post-exercise sampling times, from 5-min to 24-hr post-exercise. Conclusion: Post-exercise time does not appear to have a significant effect on sRPE after either steady-state or interval exercise. Thus, sRPE appears to be temporally robust and is not necessarily limited to the 30-min post exercise window historically used with this technique, although the presence/absence of a cool-down period after the exercise bout may be of importance.
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This study combined overnight fluid restriction with lack of fluid intake during prolonged cycling to determine the effects of dehydration on substrate oxidation, skeletal muscle metabolism, heat shock protein 72 (Hsp72) response, and time trial (TT) performance. Nine males cycled at ~65% VO2peak for 90 min followed by a TT (6 kJ/kg BM) either with fluid (HYD) or without fluid (DEH). Blood samples were taken every 20 min and muscle biopsies were taken at 0, 45, and 90 min of exercise and after the TT. DEH subjects started the trial with a -0.6% BM from overnight fluid restriction and were dehydrated by 1.4% after 45 min, 2.3% after 90 min of exercise, and 3.1% BM after the TT. There were no significant differences in oxygen uptake, carbon dioxide production, or total sweat loss between the trials. However, physiological parameters (heart rate [HR], rate of perceived exertion, core temperature [Tc], plasma osmolality [Posm], plasma volume [Pvol] loss, and Hsp72), and carbohydrate (CHO) oxidation and muscle glycogen use were greater during 90 min of moderate cycling when subjects progressed from 0.6% to 2.3% dehydration. TT performance was 13% slower when subjects began 2.3% and ended 3.1% dehydrated. Throughout the TT, Tc, Posm, blood and muscle lactate [La], and serum Hsp72 were higher, even while working at a lower power output (PO). The accelerated muscle glycogen use during 90 min of moderate intensity exercise with DEH did not affect subsequent TT performance, rather augmented Tc, RPE and the additional physiological factors were more important in slowing performance when dehydrated. © 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society.
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Objective. The Session Rating of Perceived Extertion (RPE) is a method of measuring exercise intensity that may be useful for the quantitative assessment of exercise training programmes. However, there are inadequate data regarding the validity and reliability of the Session RPE method. This study was designed to evaluate both the validity and reliability of the Session RPE method in comparison to objective measures (%HRpeak, %HRreserve and %VO2peak) of exercise intensity. Methods. Fourteen healthy volunteers (7 male, 7 female) performed 6 randomly ordered 30-minute constant-load exercise bouts at 3 different intensities, with each intensity being repeated. Oxygen consumption (VO2) and heart rate (HR) were measured throughout each exercise bout and normalised to maximal values obtained during a preliminary maximal exercise test. Thirty minutes following the conclusion of each exercise bout, the subject rated the global intensity of the bout using a modification of the Category Ratio (CR) (0 - 10) RPE scale. This rating was compared to the mean value of objectively measured exercise intensity across the duration of the bout. Results. There were significant non-linear relationships between Session RPE and %VO2peak (R2 = 0.76), %HRpeak (R2 = 0.74) and %HRreserve (R2 = 0.71). There were no significant differences between test and retest values of %VO2peak, %HRpeak, %HRreserve and Session RPE during the easy (47 v. 47%, 65 v. 66%, 47 v. 48% and 2.0 v. 1.9), moderate (69 v. 70%, 83 v. 84%, 74 v. 75%, and 4.2 v. 4.3) and hard (81 v. 81%, 94 v. 94%, 91 v. 91% and 7.3 v. 7.4) exercise bouts. Correlations between repeated bouts for %VO2peak (r = 0.98), %HRpeak (r = 0.98), %HRreserve (r = 0.98) and Session RPE (r = 0.88) were significant and strong. Conclusions. The results support the validity and reliability of the Session RPE method of monitoring exercise intensity, although as might be predicted for a subjective method the Session RPE was less precise than the objective measures of exercise training intensity. South African Journal of Sports Medicine Vol. 18 (1) 2006: pp. 14-17
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Successful training must involve overload, but also must avoid the combination of excessive overload plus inadequate recovery. Athletes can experience short-term performance decrement, without severe psychological, or lasting other negative symptoms. This Functional Overreaching (FOR) will eventually lead to an improvement in performance after recovery. When athletes do not sufficiently respect the balance between training and recovery, Non-Functional Overreaching (NFOR) can occur. The distinction between NFOR and the Overtraining Syndrome (OTS) is very difficult and will depend on the clinical outcome and exclusion diagnosis. The athlete will often show the same clinical, hormonal and other signs and symptoms. A keyword in the recognition of OTS might be ‘prolonged maladaptation’ not only of the athlete, but also of several biological, neurochemical, and hormonal regulation mechanisms. It is generally thought that symptoms of OTS, such as fatigue, performance decline and mood disturbances, are more severe than those of NFOR. However, there is no scientific evidence to either confirmor refute this suggestion. One approach to understanding the aetiology of OTS involves the exclusion of organic diseases or infections and factors such as dietary caloric restriction (negative energy balance) and insufficient carbohydrate and/or protein intake, iron deficiency, magnesium deficiency, allergies, etc., together with identification of initiating events or triggers. In this paper, we provide the recent status of possible markers for the detection of OTS. Currently several markers (hormones, performance tests, psychological tests, biochemical and immune markers) are used, but none of them meets all criteria to make its use generally accepted.
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This study examined the association between monitoring tools, training loads, and performance in concurrent heat and hypoxia (H + H) compared with temperate training environments. A randomized parallel matched-group design involved 18 well-trained male cyclists. Participants performed 12 interval sessions (3 weeks) in either H + H (32 ± 1 °C, 50% RH, 16.6% O2 normobaric hypoxia) or control (21 °C, 50% RH, 21% O2 ), followed by a seven-session taper (3 weeks; 21 °C, 50% RH, 21% O2 ), while also maintaining external training (∼ 6-10 h/week). A 20-km time trial (TT) was completed pre- and post-training block (21 °C, 50% RH, 21% O2 ). Before each TT and once weekly, a 4-min cycle warm-up (70% 4-min mean maximum power) was completed. Visual analog scale rating for pain, recovery, and fatigue was recorded before the warm-up, with heart rate (HREx ), heart rate recovery (HRR), and rating of perceived exertion (RPEWU ) recorded following. Training load was quantified using the session rating of perceived exertion (sRPE) method throughout. Overall TT improved 35 ± 47 s with moderate correlations to HRR (r = 0.49) and recovery (r = -0.55). H + H group had a likely greater reduction in HREx [ES = -0.50 (90% CL) (-0.88; 0.12)] throughout and a greater sRPE (ES = 1.20 [0.41; 1.99]), and reduction in HRR [ES = -0.37 (-0.70;-0.04)] through the overload. RPEWU was associated with weekly training load (r = 0.37). These findings suggest that recovery and HRR in a temperate environment may be used as simple measures to identify an athlete's readiness to perform. Alternatively, the relationship of RPEWU and training load suggests that perception of effort following a standardized warm-up may be a valid measure when monitoring an athlete's training response, irrespective of the training environment. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.