Content uploaded by Fernando Klitzke Borszcz
Author content
All content in this area was uploaded by Fernando Klitzke Borszcz on Mar 13, 2019
Content may be subject to copyright.
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 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: Is the Functional Threshold Power Interchangeable With the Maximal Lactate
Steady State in Trained Cyclists?
Authors: Fernando Klitzke Borszcz, Artur Ferreira Tramontin, and Vitor Pereira Costa
Affiliations: Human Performance Research Group, Center for Health and Sport Sciences,
Santa Catarina State University, Florianópolis, Brazil.
Journal: International Journal of Sports Physiology and Performance
Acceptance Date: December 24, 2018
©2019 Human Kinetics, Inc.
DOI: https://doi.org/10.1123/ijspp.2018-0572
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
Section: Original Investigation
Title: Is the functional threshold power interchangeable with the maximal lactate steady state
in trained cyclists?
Authors:
Fernando Klitzke Borszcz,1 Artur Ferreira Tramontin,1 Vitor Pereira Costa1
Affiliations:
1. Human Performance Research Group, Center for Health and Sport Sciences, Santa Catarina
State University, Florianópolis, Brazil.
Corresponding author:
Vitor Pereira Costa
Human Performance Research Group, Center for Health and Sport Sciences, Santa Catarina
State University.
Rua Pascoal Simone, 358, Coqueiros, Florianópolis, Brazil.
CEP: 88080-350
Tel: 55 (048) 3664-8600
Email: vitor.costa@udesc.br
Running head: Validity of FTP
Abstract word count: 242 words
Text-only word count: 3245 words
Figures and tables count: 2 figures and 3 tables
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
ABSTRACT
Purpose: Functional Threshold Power (FTP), determined as 95% of the average power during
a 20-minute time-trial test, is suggested as a practical test for the determination of the maximal
lactate steady state (MLSS) in cycling. Therefore, the objective of the present study was to
determine the validity of FTP in predicting MLSS. Method: Fifteen cyclists, 7 classified as
trained and 8 as well-trained (mean ± standard deviation; maximal oxygen uptake = 62.3 ± 6.4
mL/kg/min, maximal aerobic power = 329 ± 30 Watts), performed an incremental test to
exhaustion, an FTP test, and several constant load tests to determine the MLSS. The bias ±
95% limits of agreement (LoA), typical error of the estimate (TEE), and Pearson´s coefficient
of correlation (r) were calculated to assess validity. Results: For the power output measures,
FTP presented a bias ± 95% LoA of 1.4 ± 9.2%, a moderate TEE (4.7%), and nearly perfect
correlation (r = 0.91) with MLSS in all cyclists together. When divided by the training level,
the bias ± 95% LoA and TEE were higher in the trained group (1.4 ± 11.8% and 6.4%,
respectively) than in the well-trained group (1.3 ± 7.4% and 3.0%, respectively). For the heart
rate measurement, FTP presented a bias ± 95% LoA of −1.4 ± 8.2%, TEE of 4.0%, and very -
large correlation (r = 0.80) with MLSS. Conclusion: Therefore, trained and well-trained
cyclists can use FTP as a noninvasive and practical alternative to estimate MLSS.
Key-words: validity; cycling; time-trial; performance; threshold.
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
INTRODUCTION
Maximal lactate steady state (MLSS) is defined as the highest constant intensity of
exercise that can be maintained for a longer period without the continuous increase in blood
lactate concentration ([La−]), and it is the gold-standard parameter for aerobic evaluation.1–3
MLSS determination is based on several (2–5) 30-min tests performed on different days,2 thus
requiring several visits to the laboratory, which is not practical and accessible for many
athletes. In cycling one of the most well-known and controversial concepts is the Functional
Threshold Power (FTP), which is defined as the highest power that a cyclist can maintain in a
quasi-steady state without fatigue for approximately 1 hour.4 When power exceeds the FTP,
fatigue will occur much sooner, whereas power just below the FTP can be maintained
considerably longer.4 Measurement of the FTP is suggested as a practical and noninvasive test
(e.g., 60-min time-trial [TT]; FTP60) for predicting MLSS.4
The time that a trained or well-trained cyclist sustains until exhaustion at MLSS
intensity is approximately 60 min; however, there is great individual variability (range, 30–70
min).5–8 In addition, as demonstrated by Harnish et al.9 and Campbell et al.,10 the velocity at
40-km TT (approximately 60 min) has trivial differences and nearly perfect correlations (r =
0.92 and 0.99, respectively) with the velocity at the MLSS.9,10 However, a TT duration of
approximately 60 min is extremely stressful and difficult to perform in outdoor conditions.
Thus, in an attempt to make its determination more practical, a protocol composed of a specific
warm-up of 45 min and a TT of 20 min was proposed, where FTP corresponds to 95% of the
average power (FTP20).4 Recently, the FTP20 demonstrated a trivial difference and very large
correlation with FTP60 (effect size [d] = 0.14, r = 0.88), and the time to exhaustion at FTP20 (51
± 16 min)11 was close to that found at MLSS (48.2–55.2 min).5–8 In addition, FTP20
demonstrated trivial differences (d < 0.2) and moderate to nearly perfect correlations (r = 0.61–
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
0.90) with the anaerobic threshold (AnT).11,12 The AnT demarcates the highest intensity in
which the production and elimination of [La−] are in equilibrium during an incremental test.1
The validity of various versions of the FTP test, such as 8-, 20- and 60-min TT, in
relation to the AnT has already been well studied.11–14 However, although AnT measurement
is the most common test to predict MLSS,1,15 the relationship between AnT and MLSS is
conflicting.1,15,16 In addition, FTP has been used to determine heart rate (HR) and power output
training zones, calculate metrics of training load and intensity (i.e., Training Stress Score [TSS]
and Intensity Factor [IF]), and prescribe the training intensity, with the assumption of
interchangeability with the MLSS. However, to the best of our knowledge, no study has tested
the validity of FTP20 against MLSS. Moreover, FTP is based on a TT test; therefore, the training
level and experience of the cyclist is a key factor in FTP determination. As demonstrated by
Valenzuela et al.,12 FTP20 underestimated the AnT by 6.5% and 1.6% in cyclists classified as
recreationally trained (RT) and trained (T),17 respectively. The study showed a very large
correlation (r = 0.77) between the differences (bias) in FTP20 and AnT and the maximal aerobic
power (MAP).12
Therefore, the objective of the present study was to determine the validity of FTP20 for
the prediction of MLSS and in the cyclists divided by training levels classified as trained and
well-trained.
METHODS
Participants
The criteria for participation in the study were cyclists who had trained for at least 3
years, been competing regularly, and performed the FTP20 protocol at least once previously.
Thus, 15 male cyclists (mean ± standard deviation [SD]: age: 35.3 ± 5.0 years, weight: 75.0 ±
7.4 kg, and height: 176.0 ± 7.4 cm) fulfilled the criteria for participation. The cyclists were
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
classified according to training level by using the maximal oxygen uptake (V
̇O2max) value
relative to body weight (mL/kg/min) as a criterion according to the guideline of de-Pauw et
al.17 Thus, 7 cyclists were classified as trained (T; V
̇O2max 55 − 64.9 mL/kg/min); and 8, as
well trained (WT; V
̇O2max 65 − 71 mL/kg/min). After verbal and written explanations of the
procedures, all the subjects signed an informed consent approved by the institutional ethics
committee.
Design
To investigate the concurrent validity between FTP20 and MLSS, the cyclists performed
in this order: an incremental test, the FTP20 protocol, and several tests to determine the MLSS.
The riders were asked to refrain from strenuous exercise in the 48 h preceding each test.
Participants were given at least 2 and a maximum of 4 days between visits and all tests were
completed within 3 weeks. All the tests were conducted under standardized laboratory
conditions of 20°C and 40–50% relative humidity, and the tests were performed in the same
time of day (± 1 h). The tests were performed on the electrically braked bicycle ergometer
Velotron (Dynafit Pro, Racer Mate Inc, WA, USA), which was modified with a racing saddle,
adjustable stem, and the subject’s pedal system in the first visit and replicated in further tests.
The accuracy of Velotron is described elsewhere.18
PROCEDURES
Incremental test
The incremental test was started at 100 W, with increments every 3 min of 30 W until
maximum voluntary exhaustion. During the test, HR and oxygen uptake (V̇O2) were
continuously measured (Quark PFT Ergo, Cosmed, Rome, Italy). The V
̇O2 data were plotted
as a function of the power in an average of 30 sec, and the highest value was considered the
V
̇O2max. Maximal HR (HRmax) was defined as the highest individual value. Maximum
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
aerobic power (MAP) was determined as the load (W) corresponding to the last stage
completed by the subject during the incremental test. If the last stage was not completed, MAP
was determined in accordance with the method of Kuipers et al.19
Functional threshold power
The FTP20 protocol was performed using the RacerMate Interactive 3D software
(RacerMate Inc, WA, USA). During the test, the participants could view their progress over
the course on a computer monitor and be provided with information on the time completed and
gear selected; all other information was blinded, no verbal encouragement was provided, and
water was allowed ad libitum.20 FTP20 was performed in accordance with the procedure
described by Allen and Coggan.4 The warm-up duration was 50 min as follows: a) 20 min at a
self-selected easy intensity; b) 3- 1-min fast pedaling accelerations (100–105 rpm) with a 1-
min recovery between the efforts; c) 5 min at a self-selected easy intensity; d) 5-min time-trial;
e) 10 min at a self-selected easy intensity; and 5 min of resting. The main part of the test
consisted of a 20-min TT, where the participants were asked to produce the highest mean power
output possible for 20 min and adopt their personal pacing strategies. During the test, HR was
continuously monitored using the standard HR telemetry (RS800CX, Polar Electro Oy,
Finland). FTP20 and FTP20 HR (FTP20 HR) were determined as 95% of the mean power output
and HR of the 20-min TT, respectively.
Maximal lactate steady state
For determination of the MLSS, several constant load submaximal tests with a duration
of 30 min were performed on different days at an interval of at least 48 h. Prior to each test, a
warm-up of 5 min was performed at 100 W. MLSS was considered the highest exercise
intensity in which [La−] did not show an increase of >1 mmol/L during the final 20 min of the
test.2 The intensity of the first test corresponded to the FTP20 (95% of the mean power of the
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
20-min TT). If during this test, a steady state or decrease in [La−] was observed, the intensities
of the subsequent tests were increased by 5% until the steady state of [La−] could not be
observed. If the [La−] during the first test did not show a steady state and/or the exhaustion of
the cyclist occurred before the end of the 30-min period, the subsequent intensities were
decreased by 5%. Blood samples of 25 μL from the ear lobe were collected before each exercise
and every 10 min during the MLSS testing for further determination of [La−] (YSI 2700 Stat
Plus, Yellow Springs, OH, USA). MLSS HR (MLSSHR) was determined as the average value
of the last 20 min of the constant load trial. MLSS was determined with a precision of 5%.
Statistical analyses
The descriptive statistics are presented as means ± SD or 90% confidence interval (90%
CI). A spreadsheet was used for the analysis of concurrent validity.21 Before the analysis, data
were transformed using the natural logarithm to reduce nonuniformity.22 Thus, we calculate
the following between MLSS and FTP20: a) Cohen’s23 (d) effect sizes; b) the Pearson’s
correlation coefficient (r); c) the typical error of the estimate (TEE; also called standard error
of estimate); d) the standardized TEE (TEEs), calculated as TEE in raw units divided by the
SD of the values of the MLSS predicted by the FTP20;21 and e) the bias ±95% of limits of
agreement (1.96 SD of the differences [LoA]) of the Bland and Altman analysis.24 Cohen’s23
d effect sizes and unpaired Student’s t tests were used to compare the magnitude of the
differences between the groups. The d values were interpreted using the following scale: <0.20
(trivial), 0.2–0.6 (small), 0.6–1.2 (moderate), 1.2–2.0 (large), 2.0–4.0 (very large), and >4.0
(extremely large).22 Correlation coefficients were interpreted as follows: <0.09 (trivial), 0.1–
0.29 (small), 0.30–0.49 (moderate), 0.50–0.69 (large), 0.70–0.89 (very large), 0.90–0.99
(nearly perfect), and 1 (perfect).22 To interpret the magnitude of the TEEs, half of Cohen’s d
thresholds should be calculated and interpreted as follows: <0.1 (trivial), 0.1–0.3 (small), 0.3–
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
0.6 (moderate), 0.6–1.0 (large), 1.0–2.0 (very large), and >2.0 (extremely large).25 For the
Student’s t tests, the statistical significance was set at P < 0.05.
RESULTS
The cyclists’ overall characteristics and classification by training level are presented in
Table 1. The differences between the WT and T cyclists were trivial to moderate and not
statistically significant (P > 0.05) in any of the parameters measured in absolute units.
However, when normalized by body weight, the parameters were statistically significant (P <
0.05) and large to very large greater (d = 1.73 to 2.76) in the WT group in relation to the T
group.
For all the cyclists, the difference was trivial (d < 0.2), the bias ±95% LoA was 1.4 ±
9.2%, TEE was moderate (4.7%), and the correlation was nearly perfect (r = 0.91) between
FTP20 and MLSS for power output measure. The FTP20 power output occurred at the same
intensity of the MLSS in 6 cyclists (bias = 0%), underestimated by 5% in 3 cyclists,
overestimated by 5% in 5 cyclists, and overestimated by 10% in 1 cyclist (Figure 1; Table 2).
Considering the division of the training level, the bias between FTP20 and MLSS was
similar in T and WT groups (1.4% and 1.3%, respectively). However, the ±95% LoA and TEE
were 1.6 (90% CI, 0.9–3.1) and 2.1 (90% CI, 1.1–4.1) times higher in the T group than in the
WT group, respectively. The WT group showed a higher association with the power output
measures (r = 0.94) than the T group (r = 0.91; Table 2).
The bias ±95% LoA between FTP20HR and MLSSHR was −1.4 ± 8.2%, with a TTE of
4.0% and r of 0.80 (n = 15). The validity of the HR measures is presented in Table 3 and Figure
1C, D.
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
It is interesting that we found a large correlation (r = 0.67; 90% CI, 0.32–0.86) between
FTP20 − MLSS bias (%) and the 5-min TT performance (% of MLSS) performed during the
warm-up (Figure 2).
DISCUSSION
This study demonstrates a trivial difference, nearly perfect correlation and acceptable
prediction errors between FTP20 determined in accordance with the protocol of Allen and
Coggan4 and the MLSS.
MLSS is not a practical method because it involves performing several tests on different
days. Thus, for at least 40 years, more practical prediction tests have been studied.15 To our
knowledge, this is the first study to confront the validity of FTP in predicting MLSS in cyclists.
Previously the prediction of MLSS from a 40-km TT test (i.e., FTP60) was based on the
measurement of speed,9,10 which is not a reliable measure of intensity in cycling.26 Moreover,
a 60-min TT test is not practical based on the complexity and psychological and physical stress
elicited by a test of a longer duration. On the other hand, FTP20 has only 25 min of accumulated
maximum effort (the 5-min TT inserted in the warm-up and 20-min TT), so it is a more practical
test.
When analyzing the individual results, we verified that in 14 cyclists, the differences
between the FTP20 and the MLSS were within the range of 5% overestimation or
underestimation, and only 1 rider presented a difference of 10%, generating a bias ±95% LoA
of 1.4 ± 9.2% (Figure 1B). Thus, the results of the present study seem difficult to accept as
valid because intensities approximately 5% higher than the MLSS, the physiological steady
state (i.e., [La−]), did not occur and some subjects did not complete the 30-min period.2,27
However, the most common test to predict MLSS is the incremental test using AnT.1 When we
analyzed several studies that tested the validity of various AnT methods determined from [La−]
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
or ventilatory responses in predicting MLSS, the ±95% LoA, which accounts for 95% of
individual differences between measures,24 ranged from 9.5% to 43%.16,28–31 Continuing with
the MLSS prediction tests, critical power (CP) is commonly used and presents a ±95% LoA of
8.6% to 19.0%.27,32,33 Therefore, the results for random errors of prediction from FTP20 are near
or lower than those commonly found in the literature for methods used to predict MLSS.
Furthermore, in their book entitled, “Training and Racing with a Power Meter”, Allen
and Coggan4 suggested measurement of CP as the practical test for determining FTP. Both the
20-min TT and CP are reliably measured in the field.36,37 However, the CP value is dependent
on the duration of the tests performed and the mathematical model used. The CP determined
in tests between 1–15 and 1–20 min from the linear and hyperbolic functions of the two-
parameter models, respectively, overestimated the MLSS by approximately 9%.27,32 However,
by using tests between 1–24 min and the hyperbolic function of the three-parameter model, a
trivial difference between MLSS and CP was found (2 ± 12 W, mean ± SD).33 MacInnis et al.34
using only 4- and 20-min TTs and the CP linear model, found that CP overestimated FTP60 by
~5%. Therefore, using the CP determined in the short tests such as FTP, the training load
metrics (i.e., TSS, IF…), and the training zones might have important changes.
Determination of FTP20 is based on a performance test (i.e., TT); thus, the training level
and experience of the cyclist is a key factor in the test result. In the present study, for both
cyclists classified as T and WT, the systematic error of prediction was trivial (d < 0.2 and bias
= 1.3–1.4%). Nevertheless, random errors of prediction were higher in the T group than in the
WT group (TEE = 6.4% and 3%; ±95% LoA = 7.4% and 11.8%). Recently, Valenzuela et al.12
compared FTP20 with AnT (Dmax method) in cyclists classified on the basis of the MAP
(W/kg) as T and RT, and verified that random errors of prediction were similar for both the T
and RT cyclists (±95% LoA = 7.8% and 8.3%, respectively). However, in the RT and T cyclists,
FTP20 underestimated the AnT by 6.8% and 1.6%, respectively. An important point to
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
emphasize is that all cyclists in the present study had performed the FTP20 protocol 1 to 4 times
previously, so they were already familiar with the protocol.
In FTP20, trivial differences were found from MLSS, AnT,11,12 and FTP6011 by using the
subtraction of 5% of the mean power of the 20-min TT, based on the complete warm-up
protocol proposed by Allen and Coggan.4 However, recently, MacInnis et al.34 showed that
FTP60 corresponded to 90% of the mean power of the 20-min TT when the warm-up before the
20-min TT was performed at moderate intensity for 15 min. Thus, the subtraction of 5% of the
average power of the 20-min TT is recommended when the warm-up protocol according to
Allen and Coggan4 is performed. According to Allen and Coggan,4 the main goal of the 5-min
TT incorporated in the warm-up is to “‘open’ up the legs for the rest of the effort”. We found
a large association (r = 0.67) between the bias % between FTP20 and MLSS and the
performance in the 5-min TT (% of MLSS; Figure 2). These results demonstrate that cyclists
who sustained the highest percentage in relation to MLSS during the 5-min TT tended to also
sustain a greater percentage in relation to the MLSS during the 20-min TT. These results can
be explained by the anaerobic capacity of cyclists, as demonstrated by de Souza et al.,35 where
cyclists who had a higher anaerobic capacity had a higher MAP (i.e., the power around the 5-
min TT).
Although power output is the best measure of intensity in cycling,26 there are still
cyclists who do not have power meters. In addition, training intensity is also prescribed and
monitored on the basis of HR. Allen and Coggan4 suggested five HR zones of intensity based
on the percentages of FTP20HR. The results of this study showed a bias ±95% LoA of −1.4 ±
8.2%, between HR corresponding to MLSS and FTP20. These results were smaller than those
reported previously between FTP20 and FTP60 (2.5 ± 10.5%) and between FTP20 and AnT 1.3
± 11.9% for HR measurement.11 However, in spite of the accuracy between the HR results, the
use of HR should be interpreted with caution because several factors can alter HR values, such
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
as the drastic increase in HR during prolonged exercise, especially in hot environments, which
is a phenomenon described as cardiac drift.26
To determine the MLSS, several visits to the laboratory were necessary; therefore,
determining this marker in athletes was a difficult task due to the need to change the training
routine for a relatively long period. Thus, the limitations in the present study refer to the
inclusion of non-professional cyclists; however, all the participants of this study had training
routines and participated in regular competitions. In addition, the limitation of the low number
of subjects (n = 15). According to Hopkins,21 a validity study must have at least 50 subjects to
have a good inference capacity for the population. Moreover, the prediction equation could be
used to calibrate the values.
PRACTICAL APPLICATIONS
This study has important practical implications because FTP is a key metric for
determining training zones and training load monitoring (i.e., IF, TSS, and derivates metrics).
Thus, on the basis of the perspective that the prediction errors between FTP and MLSS are
equal to or even smaller than those commonly reported in the literature, coaches and cyclists
can use FTP20 as an estimate of MLSS. However, we suggest that cyclists should be previously
familiar with the FTP20 protocol.
CONCLUSION
The present study demonstrates trivial differences, nearly-perfect correlation, and
moderate random errors of prediction between FTP20 and MLSS in T and WT cyclists. In this
way, T and WT cyclists and coaches can use FTP20 as a noninvasive and practical alternative
for estimating MLSS.
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
REFERENCES
1. Faude O, Kindermann W, Meyer T. Lactate threshold concepts: How valid are they?
Sport Med. 2009;39(6):469-490. doi:10.2165/00007256-200939060-00003
2. Beneke R. Methodological aspects of maximal lactate steady state—implications for
performance testing. Eur J Appl Physiol. 2003;89(1):95-99. doi:10.1007/s00421-
002-0783-1
3. Beneke R, Leithäuser RM, Ochentel O. Blood lactate diagnostics in exercise testing
and training. Int J Sports Physiol Perform. 2011;6(1):8-24. doi:10.1123/ijspp.6.1.8
4. Allen H, Coggan A. Training and Racing with a Power Meter. 2nd ed. Boulder:
Velopress; 2010.
5. Baron B, Noakes TD, Dekerle J, et al. Why does exercise terminate at the maximal
lactate steady state intensity? Br J Sports Med. 2008;42(10):528-533.
doi:10.1136/bjsm.2007.040444
6. Faude O, Hecksteden A, Hammes D, et al. Reliability of time-to-exhaustion and
selected psycho-physiological variables during constant-load cycling at the maximal
lactate steady-state. Appl Physiol Nutr Metab. 2017;42(2):142-147.
doi:10.1139/apnm-2016-0375
7. Teixeira AS, Grossl T, De Lucas RD, Guglielmo LGA. Cardiorespiratory response
and energy expenditure during exercise at maximal lactate steady state. Rev Bras
Cineantropometria e Desempenho Hum. 2014;16(2):212-222. doi:10.5007/1980-
0037.2014v16n2p212
8. Grossl T, de Lucas RD, de Souza KM, Guglielmo LGA. Time to exhaustion at
intermittent maximal lactate steady state is longer than continuous cycling exercise.
Appl Physiol Nutr Metab. 2012;37(6):1047-1053. doi:10.1139/h2012-088
9. Harnish CR, Swensen TC, Pate RR. Methods for estimating the maximal lactate
steady state in trained cyclists. Med Sci Sport Exerc. 2001;33(6):1052-1055.
doi:10.1097/00005768-200106000-00027
10. Campbell CSG, Souza WH, Ferreira JN, Assenço F, Simões HG. Prediction of
maximal lactate steady state velocity based on performance in a 5km cycling test.
Rev Bras Cineantropometria e Desempenho Hum. 2007;9(3):223-230.
11. Borszcz FK, Tramontin AF, Bossi AH, Carminatti LJ, Costa VP. Functional
threshold power in cyclists: Validity of the concept and physiological responses. Int
J Sports Med. 2018;39(10):737-742. doi:10.1055/s-0044-101546
12. Valenzuela PL, Morales JS, Foster C, Lucia A, de la Villa P. Is the functional
threshold power (FTP) a valid surrogate of the lactate threshold? [published
online ahead of print May 10 2018]. Int J Sports Physiol Perform. 2018.
doi:10.1123/ijspp.2018-0008
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
13. Sanders D, Taylor RJ, Myers T, Akubat I. A field-based cycling test to assess
predictors of endurance performance and establishing training zones. [published
online ahead of print May 25 2017]. J Strength Cond Res. 2017.
doi:10.1519/JSC.0000000000001910
14. Borszcz FK, Tramontin AF, de Souza KM, Carminatti LJ, Costa VP. Physiological
correlations with short, medium, and long cycling time-trial performance. Res Q
Exerc Sport. 2018;89(1):120-125. doi:10.1080/02701367.2017.1411578
15. Heck H, Beneke R. 30 jahre laktatschwellen–was bleibt zu tun? 30 years of lactate
thresholds–what remains to be done? Dtsch Zeitschirft Für Sport. 2008;59(12):297-
302.
16. Arratibel-Imaz I, Calleja-González J, Emparanza JI, Terrados N, Mjaanes JM,
Ostojic SM. Lack of concordance amongst measurements of individual anaerobic
threshold and maximal lactate steady state on a cycle ergometer. Phys Sportsmed.
2016;44(1):34-45. doi:10.1080/00913847.2016.1122501
17. de Pauw K, Roelands B, Cheung SS, de Geus B, Rietjens G, Meeusen R. Guidelines
to classify subject groups in sport-science research. Int J Sports Physiol Perform.
2013;8(2):111-122. doi:10.1123/ijspp.8.2.111
18. Abbiss CR, Quod MJ, Levin G, Martin DT, Laursen PB. Accuracy of the Velotron
ergometer and SRM power meter. Int J Sports Med. 2009;30(2):107-112.
doi:10.1055/s-0028-1103285
19. Kuipers H, Verstappen F, Keizer H, Geurten P, van Kranenburg G. Variability of
aerobic performance in the laboratory and its physiologic correlates. Int J Sports
Med. 1985;06(04):197-201. doi:10.1055/s-2008-1025839
20. Currell K, Jeukendrup AE. Validity, reliability and sensitivity of measures of
sporting performance. Sport Med. 2008;38(4):297-316. doi:10.2165/00007256-
200838040-00003
21. Hopkins WG. Spreadsheets for analisys of validity and reliability. Sportscience.
2015;19:36-42.
22. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for
studies in sports medicine and exercise science. Med Sci Sport Exerc. 2009;41(1):3-
13. doi:10.1249/MSS.0b013e31818cb278
23. Cohen J. Statiscal Power Analysis for the Behavioral Sciences. New Jersey:
Lawrance Erlbaum; 1986.
24. Bland MJ, Altman DG. Statistical methods for assessing agreement between two
methods of clinical measurement. Lancet. 1986;327(8476):307-310.
doi:10.1016/S0140-6736(86)90837-8
25. Smith TB, Hopkins WG. Variability and predictability of finals times of elite
rowers. Med Sci Sport Exerc. 2011;43(11):2155-2160.
doi:10.1249/MSS.0b013e31821d3f8e
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
26. Jeukendrup A, Van Diemen A. Heart rate monitoring during training and
competition in cyclists. J Sports Sci. 1998;16 Suppl(sup1):S91-9.
doi:10.1080/026404198366722
27. Pringle JSM, Jones AM. Maximal lactate steady state, critical power and EMG
during cycling. Eur J Appl Physiol. 2002;88(3):214-226. doi:10.1007/s00421-002-
0703-4
28. Grossl T, De Lucas RD, De Souza KM, Antonacci Guglielmo LG. Maximal lactate
steady-state and anaerobic thresholds from different methods in cyclists. Eur J Sport
Sci. 2012;12(2):161-167. doi:10.1080/17461391.2010.551417
29. Pallarés JG, Morán-Navarro R, Ortega JF, Fernánndez-Elías VE, Mora-Rodriguez
R. Validity and reliability of ventilatory and blood lactate thresholds in well-trained
cyclists. PLoS One. 2016;11(9):e0163389. doi:10.1371/journal.pone.0163389
30. Smekal G, Duvillard SP Von, Pokan R, et al. Blood lactate concentration at the
maximal lactate steady state is not dependent on endurance capacity in healthy
recreationally trained individuals. Eur J Appl Physiol. 2012;112(8):3079-3086.
doi:10.1007/s00421-011-2283-7
31. Hauser T, Adam J, Schulz H. Comparison of selected lactate threshold parameters
with maximal lactate steady state in cycling. Int J Sports Med. 2013;35(06):517-521.
doi:10.1055/s-0033-1353176
32. Mattioni Maturana F, Keir DA, McLay KM, Murias JM. Can measures of critical
power precisely estimate the maximal metabolic steady-state? Appl Physiol Nutr
Metab. 2016;41(11):1197-1203. doi:10.1139/apnm-2016-0248
33. Keir DA, Fontana FY, Robertson TC, et al. Exercise intensity thresholds: identifying
the boundaries of sustainable performance. Med Sci Sport Exerc. 2015;47(9):1932-
1940. doi:10.1249/MSS.0000000000000613
34. MacInnis MJ, Thomas ACQ, Phillips SM. The reliability of 4-min and 20-min time
trials and their relationships to functional threshold power in trained cyclists.
[published online ahead of print May 29 2018] Int J Sports Physiol Perform. 2018.
doi:10.1123/ijspp.2018-0100
35. de Souza KM, de Lucas RD, do Nascimento Salvador PC, et al. Maximal power
output during incremental cycling test is dependent on the curvature constant of the
power–time relationship. Appl Physiol Nutr Metab. 2015;40(9):895-898.
doi:10.1139/apnm-2015-0090
36. Nimmerichter A, Williams C, Bachl N, Eston R. Evaluation of a field test to assess
performance in elite cyclists. Int J Sports Med. 2010;31(3):160-166. doi:10.1055/s-
0029-1243222
37. Karsten B, Jobson SA, Hopker J, Stevens L, Beedie C. Validity and reliability of
critical power field testing. Eur J Appl Physiol. 2015;115(1):197-204.
doi:10.1007/s00421-014-3001-z
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
Figure 1. The relationship between the MLSS and FTP20 for power output (A) and heart rate
(C) measures, solid and dashed lines represent the regression line and the 90% confidence
intervals, respectively. Bias (continuous line) and the 95% limits of agreement (discontinuous
lines) between the two variables using the Bland and Altman24 analysis for power output (B)
and heart rate (D) measures.
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
Figure 2. Relationship between the 5-min TT performance (% of MLSS) and the bias between
FTP and the MLSS. Solid and dashed lines represent the regression line and the 90%
confidence intervals, respectively.
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
Table 1. Parameters determined during the incremental test, FTP20 protocol and maximal
lactate steady state.
Parameters
All cyclists
Well-
Trained
Trained
P-
value
d
Cyclists
(n)
15
8
7
V̇O2max
(L/min)
4.6 ± 0.5
4.8 ± 0.5
4.5 ± 0.4
0.210
0.68
(mL/kg/min)
62.3 ± 6.4
67.1 ± 3.9
57.5 ± 3.1
>0.001
2.76
MAP
(W)
328.6 ± 30.3
345.4 ± 20.4
330.9 ± 39.0
0.399
0.47
(W/kg)
4.6 ± 0.4
4.6 ± 0.3
4.2 ± 0.3
0.001
2.24
HRmax
(bpm)
184.7 ± 7.2
182.7 ± 6.8
186.5 ± 7.5
0.329
0.53
5-min TTa
(W)
331.9 ± 33.0
330.1 ± 34.2
333.9 ± 34.2
0.836
0.11
(W/kg)
4.5 ± 0.3
4.6 ± 0.3
4.3 ± 0.2
0.029
1.27
FTP20b
(W)
251.7 ± 26.3
257.1 ± 26.2
245.6 ± 26.9
0.417
0.43
(W/kg)
3.4 ± 0.3
3.6 ± 0.2
3.1 ± 0.2
0.001
2.19
(% of MAP)
74.3 ± 3.7
74.3 ± 3.9
74.4 ± 3.7
0.981
0.01
(bpm)
157.6 ± 10.1
156.3 ± 8.2
158.8 ± 11.9
0.659
0.24
(% of HRmax)
85.3 ± 4.3
85.6 ± 3.9
85.1 ± 4.8
0.833
0.11
MLSS
(W)
248.3 ± 25.0
253.4 ± 20.6
242.6 ± 29.9
0.439
0.42
(W/kg)
3.4 ± 0.3
3.6 ± 0.2
3.1 ± 0.3
0.009
1.73
(% of MAP)
73.3 ± 3.2
73.3 ± 3.4
73.3 ± 3.2
0.996
0.00
[La–] (mmol/L)
4.1 ± 1.0
4.1 ± 1.0
4.0 ± 1.0
0.723
0.19
(bpm)
159.8 ± 9.8
158.9 ± 4.3
160.6 ± 13.1
0.740
0.18
(% of HRmax)
86.5 ± 4.0
87.0 ± 2.2
86.1 ± 5.2
0.667
0.23
All data presented as mean ± SD; a = 5-min TT inserted in the warm-up protocol; b = determined as 95% of 20-
min TT; bpm = beats per minute; d = effect size; HRmax = maximal heart rate; L/min = liters per minute;
mL/kg/min = milliliter per kilogram of body weight per minute; MAP = maximal aerobic power; W = watts; W/kg
= watts per kilogram of weight; [La–] = blood lactate concentration
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
Table 2. Concurrent validity of FTP20 to predict MLSS based on power output measure.
Parameters
All cyclists
Well- trained
Trained
Mean difference ± SD
(% [90% CI])
1.4 ± 4.7
(0.7 – 3.5)
1.3 ± 3.8
(-1.1 – 3.9)
1.4 ± 6.0
(-2.8 – 5.8)
p-value
0.213
0.300
0.599
d (90% CI)
0.13
(-0.47 – 0.73)
Trivial
0.16
(-0.68 – 0.97)
Trivial
0.11
(-0.78 – 0.98)
Trivial
± 95% LoA (%)
9.2
7.4
11.8
TEE (% [90% CI])
4.7
(3.6 – 7.1)
3.0
(2.1 – 5.9)
6.4
(4.2 – 13.8)
TEEs (90% CI)
0.45
(0.27 – 0.78)
Moderate
0.37
(0.17 – 0.86)
Moderate
0.46
(0.19 – 1.32)
Moderate
r (90% CI)
0.91
(0.79 – 0.97)
Nearly perfect
0.94
(0.76 – 0.99)
Nearly perfect
0.91
(0.60 – 0.98)
Nearly perfect
CI = confidence interval; d = effect size; LoA = limits of agreement; SD = standard deviation; TEE = typical error
of estimate; TEEs = typical error of estimate standardized; r = coefficient of correlation
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
“Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?”
by Klitzke Borszcz F, Ferreira Tramontin A, Pereira Costa V
International Journal of Sports Physiology and Performance
© 2019 Human Kinetics, Inc.
Table 3. Concurrent validity of FTP20HR to predict MLSSHR based on heart rate measure.
Parameters
Results
Mean difference ± SD (% [90% CI])
-1.4 ± 4.2 (-3.2 – 0.5)
p-value
0.205
d (90% CI)
-0.22 (-0.82 – 0.39) Small
± 95% LoA (%)
8.2
TEE (% [90% CI])
4.0 (3.0 – 6.0)
TEEs (90% CI)
0.75 (0.43 – 1.51) Large
r (90% CI)
0.80 (0.55 – 0.92) Very large
n = 15; CI = confidence interval; d = effect size; LoA = limits of agreement; SD = standard deviation; TEE =
typical error of estimate; TEEs = typical error of estimate standardized; r = coefficient of correlation
Downloaded by AUCKLAND UNIV OF TECH LIBRARY on 01/26/19
