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Three to 5 cycling tests to exhaustion allow prediction of time to exhaustion (TTE) at power output based on calculation of critical power (CP). We aimed to determine the accuracy of CP predictions of TTE at power outputs habitually endured by cyclists. Fourteen endurance-trained male cyclists underwent 4 randomized cycle-ergometer TTE tests at power outputs eliciting (i) mean Wingate anaerobic test (WAnTmean), (ii) maximal oxygen consumption, (iii) respiratory compensation threshold (VT2), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of 5%–9%. Power outputs were 710 ± 63 W for WAnTmean, 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT2 and 247 ± 20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P < 0.001). The CP predictions overestimated TTE at WAnTmean (00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT2 (–04:18 ± 03:20 mm:ss; P < 0.05), and correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or lower power outputs (e.g., sprints and 40-km time trials). NoveltyCP calculation enables to predict TTE at any cycling power output. We tested those predictions against measured TTE in a wide range of cycling power outputs. CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.
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ARTICLE
Time to exhaustion during cycling is not well predicted by
critical power calculations
Jesus G. Pallarés, Jose R. Lillo-Bevia, Ricardo Morán-Navarro, Victor Cerezuela-Espejo,
and Ricardo Mora-Rodriguez
Abstract: Three to 5 cycling tests to exhaustion allow prediction of time to exhaustion (TTE) at power output based on
calculation of critical power (CP). We aimed to determine the accuracy of CP predictions of TTE at power outputs habitually
endured by cyclists. Fourteen endurance-trained male cyclists underwent 4 randomized cycle-ergometer TTE tests at power
outputs eliciting (i) mean Wingate anaerobic test (WAnT
mean
), (ii) maximal oxygen consumption, (iii) respiratory compensation
threshold (VT
2
), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of
5%–9%. Power outputs were 710 ± 63 W for WAnT
mean
, 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT
2
and 247 ±
20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power
output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P< 0.001). The CP predictions overestimated TTE
at WAnT
mean
(00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT
2
(–04:18 ± 03:20 mm:ss; P< 0.05), and
correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at
maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or
lower power outputs (e.g., sprints and 40-km time trials).
Novelty
CP calculation enables to predict TTE at any cycling power output.
We tested those predictions against measured TTE in a wide range of cycling power outputs.
CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.
Key words: endurance training, Wingate anaerobic test, respiratory compensation point, V
˙O
2max
, maximal lactate steady state,
cyclist.
Résumé : Trois à cinq tests de pédalage jusqu’à épuisement permettent de prédire le temps écoulé jusqu’à épuisement TTE »)
à la puissance produite sur la base du calcul de la puissance critique CP »). Nous voulons déterminer la précision des prédictions
de TTE basées sur la CP aux puissances habituellement supportées par les cyclistes. Quatorze cyclistes masculins entraînés en
endurance participent sur un cycloergomètre à 4 tests de TTE présentés aléatoirement aux puissances suivantes : (i) test
anaérobie de Wingate à la puissance moyenne WAnT
mean
»), (ii) consommation maximale d’oxygène, (iii) seuil de compensation
respiratoire (VT
2
)et(iv) lactate maximal en régime stable MLSS »). Les tests effectués deux fois présentent un coefficient de
variation de 5–9 %. Les puissances produites sont de 710 ± 63 W (WAnT
mean
), 366 ± 26 W (consommation maximale d’oxygène),
302±31W(VT
2
) et 247 ± 20 W (MLSS). Les TTE correspondants sont respectivement de 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07
et 76:05 ± 13:53 min:s. La puissance produite associée à CP n’est que de 2 % inférieure à MLSS (242 ± 19 vs 247 ± 20 W; P< 0,001).
Les prédictions de CP surestiment TTE à WAnT
mean
(00:24 ± 00:10 min:s) et MLSS (04:41 ± 11:47 min:s), sous-estiment TTE à VT
2
(–04:18 ± 03:20 min:s); P< 0,05) et TTE correctement à consommation maximale d’oxygène. En bref, la CP prédit avec précision
la puissance au test de MLSS et TTE au test de la consommation maximale d’oxygène. Cependant, on ne doit pas utiliser CP pour
estimer le temps écoulé jusqu’à épuisement chez les cyclistes entraînés à des puissances supérieures ou inférieures (par exemple,
sprints et contre-la-montre de 40 km). [Traduit par la Rédaction]
Les nouveautés
Le calcul de CP permet de prédire le TTE qu’importe la puissance de pédalage.
Nous avons testé ces prédictions par rapport au TTE mesuré dans une large gamme de puissance de pédalage.
CP prédit de manière appropriée le TTE au test de la consommation maximale d’oxygène mais se trompe aux puissances de
pédalage supérieures et inférieures.
Mots-clés : entraînement d’endurance, test anaérobie de Wingate, seuil de compensation respiratoire, V
˙O
2max
, lactate maximal en
régime stable, cycliste.
Received 4 September 2019. Accepted 31 December 2019.
J.G. Pallarés, J.R. Lillo-Bevia, R. Morán-Navarro,* and V. Cerezuela-Espejo. Human Performance and Sports Science Laboratory. University of
Murcia, 30720, Murcia, Spain.
R. Mora-Rodriguez. Exercise Physiology Laboratory at Toledo. University of Castilla-La Mancha, Avda Carlos III, s/n, 47051, Toledo, Spain.
Corresponding author: Ricardo Mora-Rodríguez (email: ricardo.mora@uclm.es).
*Ricardo Mora-Rodríguez currently serves as an Associate Editor; peer review and editorial decisions regarding this manuscript were handled by Christopher Irwin
and Wendy Ward.
Copyright remains with the author(s) or their institution(s). Permission for reuse (free in most cases) can be obtained from copyright.com.
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Appl. Physiol. Nutr. Metab. 45: 753–760 (2020) dx.doi.org/10.1139/apnm-2019-0637 Published at www.nrcresearchpress.com/apnm on 14 January 2020.
... In the past, researchers often preselected one model form to derive CP and/or did not conduct an appropriate evaluation of the models' goodness of fit [3,29,34,41,42,45]. Exemplary of this is the misuse of R 2 values to evaluate the model fit of the hyperbolic P-t model. ...
... It is noticeable that many of the investigated studies did not perform a thorough evaluation of model fits for CP, meaning that they did not compare different model outputs and/or did not use a priori criteria before accepting parameter estimates [3,29,41,42,45]. In some studies, while the SEE and/or CV% associated with CP and W′ seemed sufficiently accurate at the group level [44,47], there may have been a substantial level of variance in these quality measures within individuals. ...
... In these studies, it is questionable whether the CP estimates can be considered valid. To the same extent, most studies did not apply appropriate and personal time criteria and thus often included trials outside the recommended time range [3,29,[41][42][43][44][45][46]. We found that some studies reported an average time range, but this was not checked at the individual level so that it is not clear that all individual models conformed to the desired criteria of exercise durations that are likely to result in the attainment of V O 2max (i.e., indicative of the severe-intensity domain). ...
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... Moreover, the TSS cannot be individualized nor adjusted to a particular exercise intensity zone, both being critical factors for an accurate management of the relationship between training dose and training outcome or response [32]. In addition, while FTP has been suggested as a non-invasive and practical alternative for estimating physiologically relevant events, such as maximal lactate steady state (MLSS) [36], recent investigations demonstrate a large variability between traditionally established events (e.g., ventilatory and lactate thresholds) [37,38]. Thus, two individuals cycling at 80% of their FTP could, be exercising at dissimilar physiological levels, resulting in different intensity, adaptations, fatigue, and recovery times [39,40]. ...
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... Subjects chose their preferred cadence and were blinded to any variable that could affect their performance (e.g., elapsed time or heart rate). The test started with a 10-minute warmup (5 minutes at 80% and then at 90% of the PO attained at the VT, respectively) and ended when pedaling cadence fell below 60 rpm [33]. The rate of perceived exertion scale (6-20 points) was administered to cyclists at the end of each test to verify maximality. ...
... Changes produced by both RT groups in time-to-exhaustion at RCP could be considered meaningful in practice (especially those achieved by the on-bike group, ∆ = 14.2%, Figure 4B). However, the high variability of this parameter, also found by previous research [33], could have hindered the achievement of actual statistical significance. The enhancements our study found for this parameter would agree in magnitude and direction with those reported by previous studies for time-to-exhaustion (+17%) [35] or time trials (+6%) [37] at similar Regarding muscle-tendon adaptations, our findings are in line with previous research supporting the effectiveness of high-intensity squat training, even performed far from failure, to increase QUAD CSA [25]. ...
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... 30 These findings support previous research indicating that CP does not represent a "fatigueless" task. 6,10,75,76 The time-saving 3-min all-out CP test overestimated the CP by 14.6% compared to the preferred non-linear 3 model. Since CP demarcates the moderate and high exercise intensity domains, cycling at a power output 14.6% greater than this boundary will most definitely result in (premature) exhaustion. ...
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... KG ve MLD'nin benzer iş yüklerine karşılık geldiğini gösteren çok sayıda çalışma bulunsa da şiddetli egzersiz alanının alt sınırı olarak kabul edilen bir diğer parametre olan KG'nin MLD'ye ait iş yükünü yüksek tahmin ettiğini gösteren çalışmalar da mevcuttur. [22][23][24][25][26][27][28] Bu türden çalışmalardan elde edilen bulgulara göre KG ve MLD'ye karşılık gelen egzersiz şiddetleri arasında %8-15 fark vardır. Diğer yandan, çalışmamızın 2. bir sınırlılığı, yöntemde uygulanan kademeli egzersiz testinin Caputo ve Denadai tarafından ilk orijinal çalışmalarında uyguladıkları gibi üçer dk'lık kademe artışları içermemesidir. 3 Bu çalışmada uygulanan ka-demeli test, Caputo ve Denedai'nin uyguladığı gibi tüketici ancak yük artışlarının her iki sn'de bir uygulandığı rampa testidir. ...
... It is well known that the hyperbolic model describes exercise durations in the 2-15 minute range well but exhibits unrealistic behaviour outside this range (see, e.g., Vandewalle et al., 1997;Jones et al., 2019;Pallarés et al., 2020). ...
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Borszcz, FK, de Aguiar, RA, Costa, VP, Denadai, BS, and de Lucas, RD. Agreement between maximal lactate steady state and critical power in different sports: A systematic review and Bayesian's meta-regression. J Strength Cond Res 38(6): e320-e339, 2024-This study aimed to systematically review the literature and perform a meta-regression to determine the level of agreement between maximal lactate steady state (MLSS) and critical power (CP). Considered eligible to include were peer-reviewed and "gray literature" studies in English, Spanish, and Portuguese languages in cyclical exercises. The last search was made on March 24, 2022, on PubMed, ScienceDirect, SciELO, and Google Scholar. The study's quality was evaluated using 4 criteria adapted from the COSMIN tool. The level of agreement was examined by 2 separate meta-regressions modeled under Bayesian's methods, the first for the mean differences and the second for the SD of differences. The searches yielded 455 studies, of which 36 studies were included. Quality scale revealed detailed methods and small samples used and that some studies lacked inclusion/exclusion criteria reporting. For MLSS and CP comparison, likely (i.e., coefficients with high probabilities) covariates that change the mean difference were the MLSS time frame and delta criteria of blood lactate concentration, MLSS number and duration of pauses, CP longest predictive trial duration, CP type of predictive trials, CP model fitting parameters, and exercise modality. Covariates for SD of the differences were the subject's maximal oxygen uptake, CP's longest predictive trial duration, and exercise modality. Traditional MLSS protocol and CP from 2-to 15-minute trials do not reflect equivalent exercise intensity levels; the proximity between MLSS and CP measures can differ depending on test design, and both MLSS and CP have inherent limitations. Therefore, comparisons between them should always consider these aspects.
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The aims of this study were (1) to establish the best fit between ventilatory and lactate exercise performance parameters in running and (2) to explore novel alternatives to estimate the maximal aerobic speed (MAS) in well-trained runners. Twenty-two trained male athletes ( V ˙ O2max 60.2 ± 4.3 ml·kg·min-1) completed three maximal graded exercise tests (GXT): (1) a preliminary GXT to determine individuals' MAS; (2) two experimental GXT individually adjusted by MAS to record the speed associated to the main aerobic-anaerobic transition events measured by indirect calorimetry and capillary blood lactate (CBL). Athletes also performed several 30 min constant running tests to determine the maximal lactate steady state (MLSS). Reliability analysis revealed low CV (<3.1%), low bias (<0.5 km·h-1), and high correlation (ICC > 0.91) for all determinations except V-Slope (ICC = 0.84). Validity analysis showed that LT, LT+1.0, and LT+3.0 mMol·L-1 were solid predictors of VT1 (-0.3 km·h-1; bias = 1.2; ICC = 0.90; p = 0.57), MLSS (-0.2 km·h-1; bias = 1.2; ICC = 0.84; p = 0.74), and VT2 (<0.1 km·h-1; bias = 1.3; ICC = 0.82; p = 0.9l9), respectively. MLSS was identified as a different physiological event and a midpoint between VT1 (bias = -2.0 km·h-1) and VT2 (bias = 2.3 km·h-1). MAS was accurately estimated (SEM ± 0.3 km·h-1) from peak velocity (Vpeak) attained during GXT with the equation: MASEST (km·h-1) = Vpeak (km·h-1) * 0.8348 + 2.308. Current individualized GXT protocol based on individuals' MAS was solid to determine both maximal and submaximal physiological parameters. Lactate threshold tests can be a valid and reliable alternative to VT and MLSS to identify the workloads at the transition from aerobic to anaerobic metabolism in well-trained runners. In contrast with traditional assumption, the MLSS constituted a midpoint physiological event between VT1 and VT2 in runners. The Vpeak stands out as a powerful predictor of MAS.
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Lillo-Beviá, JR, Courel-Ibáñez, J, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, and Pallarés, JG. Is the functional threshold power a valid metric to estimate the maximal lactate steady state in cyclists? J Strength Cond Res XX(X): 000-000, 2019-The aims of this study were to determine (a) the repeatability of a 20-minute time-trial (TT20), (b) the location of the TT20 in relation to the main physiological events of the aerobic-anaerobic transition, and (c) the predictive power of a list of correction factors and linear/multiple regression analysis applied to the TT20 result to estimate the individual maximal lactate steady state (MLSS). Under laboratory conditions, 11 trained male cyclists and triathletes (V[Combining Dot Above]O2max 59.7 ± 3.0 ml·kg·min) completed a maximal graded exercise test to record the power output associated with the first and second ventilatory thresholds and V[Combining Dot Above]O2max measured by indirect calorimetry, several 30 minutes constant tests to determine the MLSS, and 2 TT20 tests with a short warm-up. Very high repeatability of TT20 tests was confirmed (standard error of measurement of ±3 W and smallest detectable change of ±9 W). Validity results revealed that MLSS differed substantially from TT20 (bias = 26 ± 7 W). The maximal lactate steady state was then estimated from the traditional 95% factor (bias = 12 ± 7 W) and a novel individual correction factor (ICF% = MLSS/TT20), resulting in 91% (bias = 1 ± 6 W). Complementary linear (MLSS = 0.7488 × TT20 + 43.24; bias = 0 ± 5 W) and multiple regression analysis (bias = 0 ± 4 W) substantially improved the individual MLSS workload estimation. These findings suggest reconsidering the TT20 procedures and calculations to increase the effectiveness of the MLSS prediction.
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The dissociation between constant-work rate V̇O 2 and ramp-V̇O 2 at a given work rate might be mitigated during slow increasing ramp-protocols. This study characterized the V̇O 2 dynamics in response to five different ramp-protocols and constant-work rate trials at the maximal metabolic steady-state (MMSS), to characterize i) the V̇O 2 gain (G) in the moderate, heavy, and severe domains, ii) the mean response time of V̇O 2 (MRT), iii) the work rates at lactate threshold (LT) and respiratory compensation-point (RCP). Eleven young individuals performed five ramp-tests (5, 10, 15, 25, 30 W·min ⁻¹ ), 4-5 time-to-exhaustions for critical power estimation, and 2-3 constant-work rate trials for confirmation of the work rate at MMSS. G was greatest during the slowest ramp, and progressively decreased with increasing ramp-slopes (from ~12 to ~8 ml·min ⁻¹ ·W ⁻¹ ) ( P<0.05). The MRT was smallest during the slowest ramp-slopes and progressively increased with faster ramp-slopes (1±1, 2±1, 5±3, 10±4, 15±6 W, P<0.05). After "left-shifting" the ramp-V̇O 2 by the MRT, the work rate at LT was constant regardless of the ramp-slope (~150W) ( P>0.05). The work rate at MMSS was 215±55W and was similar and high correlated with the work rate at RCP during the 5 W·min ⁻¹ ramp ( P>0.05) (r = 0.99; CCC = 0.99; bias = -3 W; RMSE = 6W). Findings showed that the dynamics of V̇O 2 (i.e., G) during ramp-exercise explain the apparent dichotomy existing with constant-work rate exercise. When these dynamics are appropriately "resolved", LT is constant regardless of the ramp-slope of choice and RCP and MMSS display minimal variations between each other.
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Common methods to prescribe exercise intensity are based on fixed-percentages of maximum rate of oxygen uptake (V[Combining Dot Above]O2max), peak work rate (WRpeak), maximal heart rate (HRmax). However, it is unknown how these methods compare to the current models to partition the exercise intensity spectrum. Purpose: Thus, the aim of this study was to compare contemporary gold-standard approaches for exercise prescription based on fixed-percentages of maximum values to the well established but underutilized "domain" schema of exercise intensity. Methods: One hundred individuals participated in the study (women=46; men=54). A cardiopulmonary ramp-incremental test was performed to assess V[Combining Dot Above]O2max, WRpeak, HRmax, and the lactate threshold (LT), and submaximal constant-work rate trials of 30-min duration to determine the maximal lactate steady-state (MLSS). The LT and MLSS were used to partition the intensity spectrum for each individual in three domains of intensity: moderate, heavy, and severe. Results: V[Combining Dot Above]O2max in women and men was 3.06±0.41 L·min and 4.10±0.56 L·min, respectively. LT and MLSS occurred at a greater %V[Combining Dot Above]O2max and %HRmax in women compared to men (P<0.05). The large ranges in both sexes at which LT and MLSS occurred on the basis of %V[Combining Dot Above]O2max (LT=45-74%; MLSS=69-96%), %WRpeak (LT=23-57%; MLSS=44-71%), and %HRmax (LT=60-90%; MLSS=75-97%) elicited large variability in the number of individuals distributed in each domain at the fixed-percentages examined. Conclusions: Contemporary gold-standard methods for exercise prescription based on fixed-percentages of maximum values conform poorly to exercise intensity domains and thus do not adequately control the metabolic stimulus.
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During ramp-incremental exercise, the mean response time (MRT) of oxygen uptake (V˙O2) represents the time delay for changes in muscle V˙O2 to be reflected at the level of the mouth and is generally calculated by linear (MRTLIN) and monoexponential (τ') fitting of V˙O2 data. However, these methods yield MRT values that are highly variable from test-to-test. Purpose: Therefore, we examined the validity and the reproducibility of a novel method to calculate the MRT. Methods: On two occasions, 12 healthy men (age, 30 ± 10 yr; V˙O2max: 4.14 ± 0.47 L·min, 53.5 ± 7.3 mL·kg·min) performed a ramp-incremental cycling test (30 W·min) that was preceded by a step transition to 100 W. The ramp power output corresponding to the steady-state V˙O2 at 100 W was determined and the difference between that power output and 100 W was converted to time to quantify the MRT (MRTSS). Results: The values of MRTLIN, τ', and MRTSS were 28 ± 16 s, 27 ± 12 s, and 26 ± 11 s, respectively, which were not different (P > 0.05) from each other. However, compared to the MRT parameters derived from the fitting-based methods, MRTSS had a higher correlation coefficient (R = 0.87) and a smaller coefficient of variation (15% ± 9%) from test-to-test. Conclusions: In conclusion, the novel method proposed in the current study was found to be valid and highly reproducible in a test-retest design. Therefore, we advocate the use of this approach when a precise and accurate determination of the MRT is needed to properly align the V˙O2 data with power output during ramp-incremental exercise.
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The relationship between exercise intensity and time to task failure (P-T relationship) is hyperbolic, and characterized by its asymptote (critical power[CP]) and curvature constant (W'). The determination of these parameters is of interest for researchers and practitioners, but the testing protocol for CP and W' determination has not yet been standardized. Conventionally, a series of constant work rate (CWR) tests to task failure have been used to construct the P-T relationship. However, the duration, number, and recovery between predictive CWR and the mathematical model (hyperbolic or derived linear models) are known to affect CP and W'. Moreover, repeating CWR may be deemed as a cumbersome and impractical protocol. Recently, CP and W' have been determined in field and laboratory settings using time trials, but the validity of these methods has raised concerns. Alternatively, a 3-minute all-out test (3MT) has been suggested, as it provides a simpler method for the determination of CP and W', whereby power output at the end of the test represents CP, and the amount of work performed above this end-test power equates to W'. However, the 3MT still requires an initial incremental test and may overestimate CP. The aim of this review is, therefore, to appraise current methods to estimate CP and W', providing guidelines and suggestions for future research where appropriate.