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Is the Functional Threshold Power a Valid Metric to Estimate the Maximal Lactate Steady State in Cyclists?
Abstract
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.
... As a result, gold-standard testing on a regular basis is practically non-existent in the majority of the recreational population. 9,[15][16][17] Moreover, without additional data, it is only possible to a limited extent to draw conclusions about actual competition performance from physiological markers alone. ...
... To address these challenges, mathematical modeling techniques have emerged as both an alternative and complementary approach. These models involve mathematical equations relating performance indicators to explanatory input variables 18,19 and can be used to either approximate performance markers, 15,20 or to gain further information about actual performance related to these markers by uncovering hidden systematic patterns and correlations, providing insights beyond key parameters, and guiding training methodology. 9,21 However, it is crucial to emphasize that mathematical models should not be perceived as mere substitutes for the gold-standard method of testing. ...
... The scientific literature also presents modeling techniques specifically aimed at optimizing competition results. 15,16,22,23 By entering relevant variables and quantifying the associated performance characteristic, it becomes possible to predict performance outcomes. These equations allow for comparisons between various situations and individuals, enabling the identification of individual effects through the manipulation of input values. ...
Physical performance in cycling is commonly evaluated with laboratory-based performance markers. However, these markers are not monitored on a regular basis, mainly due to the high costs of testing equipment, invasive sampling and time-intensive protocols. The use of mathematical modeling offers a promising alternative allowing for consistent performance monitoring, identification of influential variables affecting performance, and facilitation of planning, monitoring, and predictive analysis. Wearable technology, such as physiological and biomechanical sensors, can be integrated with mathematical models to enhance the practicality of performance monitoring and enable real-time feedback and personalized training recommendations. In this systematic review, we attempted to provide an overview of the developments in predicting and modeling of performance in cycling and their respective practical applications. The PRISMA framework yielded 52 studies that met the inclusion criteria. The models were discussed according to their modeling goal: characterizing kinetics, alternatives to the gold-standard, training control, observing training effects, predicting competitive performance and optimizing performance. Field-based models and technological advancements were highlighted as solutions to the limitations of gold-standard testing. Due to the lower accuracies of modeling techniques, the gold-standard laboratory-based methods of testing will not be replaced by mathematical models. However, models do form a more practical alternative for regular monitoring and a powerful tool for training and competition optimization. A modeling technique needs to be individualized to the goal and the person and be as simple as possible to allow regular monitoring. Ideally, the technique would work in the field, uses submaximal exercise intensities and integrates technological advancements such as wearable technology and machine learning to increase the practicality even more.
... FTP is referred to as the highest power output that can be sustained in a quasi-steady state, typically associated with the highest power developed in 60 minutes [6]. This one has been associated with the maximal lactate steady state (MLSS), although this can be jeopardized by the method used to estimate FTP from shorter distances [7][8][9][10]. The most widespread correction factor applied in cycling [9][10][11] was suggested by Allen and Coggan [6], where the mean power output (MPO) developed in a 20-min time trial (TT20) performed after a 5-min all-out effort is corrected by 95 % in order to obtain FTP. ...
... This one has been associated with the maximal lactate steady state (MLSS), although this can be jeopardized by the method used to estimate FTP from shorter distances [7][8][9][10]. The most widespread correction factor applied in cycling [9][10][11] was suggested by Allen and Coggan [6], where the mean power output (MPO) developed in a 20-min time trial (TT20) performed after a 5-min all-out effort is corrected by 95 % in order to obtain FTP. Using the power metric in running, Cartón-Llorente et al. [12] first provided FTP estimates in a group of recreationally-trained male runners through different correction ...
... Therefore, although both concepts have been conceived as the maximal work rate under a metabolic steady-state is maintained, there are discrepancies between them. On the one hand, FTP has been associated with the MLSS [7][8][9]. In this regard, Jones et al. [23] have clarified that the MLSS is located around 7 % lower than CP, like the 6 % difference here observed between FTP and CP. ...
The aims of this study were (i) to estimate the functional threshold power (FTP) and critical power (CP) from single shorter time trials (TTs) (i.e., 10, 20 and 30 minutes) and (ii) to assess their location in the power-duration curve. Fifteen highly trained athletes randomly performed ten TTs (i.e., 1, 2, 3, 4, 5, 10, 20, 30, 50 and 60 minutes). FTP was determined as the mean power output developed in the 60-minute TT, while CP was estimated in the running power meter platform according to the manufacturer's recommendations. The linear regression analysis revealed an acceptable FTP estimate for the 10, 20 and 30-minute TTs (SEE ≤ 12.27 W) corresponding to a correction factor of 85, 90 and 95%, respectively. An acceptable CP estimate was only observed for the 20-minute TT (SEE = 6.67 W) corresponding to a correction factor of 95%. The CP was located at the 30-minute power output (1.0 [-5.1 to 7.1] W), which was over FTP (14 [7.0 to 21] W). Therefore, athletes and practitioners concerned with determining FTP and CP through a feasible testing protocol are encouraged to perform a 20-minute TT and apply a correction factor of 90 and 95%, respectively.
... Furthermore, it is unclear whether a non-steady state of the BLC really represents a non-steady state of muscular lactate concentration in major power-producing muscle groups (Jones et al. 2019a). In addition to this debatable physiological basis, the validity of the MLSS concept is mainly based on bivariate correlations between PO _MLSS and PO _TT of simulated endurance competitions or time trials (Haverty et al. 1988;Jones and Doust 1998;Harnish et al. 2001;Klitzke Borszcz et al. 2019;Lillo-Beviá et al. 2019). However, such bivariate correlations are insufficient to support the assumption that MLSS is an independent predictor of supra-MLSS endurance performance and the ability to sustain a high % V O 2_TT . ...
... In this study, we examined for the first time whether PO_ MLSS or % V O 2_MLSS are independent predictors of endurance performance. Consistent with previous studies (Haverty et al. 1988;Jones and Doust 1998;Harnish et al. 2001;Klitzke Borszcz et al. 2019;Lillo-Beviá et al. 2019) PO _MLSS was highly correlated with PO _TT (see Table 2). However, as outlined in the introduction, a bivariate correlation is insufficient support for the assumption that the MLSS concept is an independent predictor of endurance performance. ...
... In our study we used a more sophisticated data analysis and statistical approach compared to previous studies (Haverty et al. 1988;Jones and Doust 1998;Harnish et al. 2001;Klitzke Borszcz et al. 2019;Lillo-Beviá et al. 2019) to analyze whether the MLSS is predictor of PO _TT independent of V O 2max and GE. However, like all of these studies we used a cross-sectional study design. ...
Purpose
There is no convincing evidence for the idea that a high power output at the maximal lactate steady state (PO_MLSS) and a high fraction of 𝑉˙O2max at MLSS (%𝑉˙O2_MLSS) are decisive for endurance performance. We tested the hypotheses that (1) %𝑉˙O2_MLSS is positively correlated with the ability to sustain a high fraction of 𝑉˙O2max for a given competition duration (%𝑉˙O2_TT); (2) %𝑉˙O2_MLSS improves the prediction of the average power output of a time trial (PO_TT) in addition to 𝑉˙O2max and gross efficiency (GE); (3) PO_MLSS improves the prediction of PO_TT in addition to 𝑉˙O2max and GE.
Methods
Twenty-one recreationally active participants performed stepwise incremental tests on the first and final testing day to measure GE and check for potential test-related training effects in terms of changes in the minimal lactate equivalent power output (∆PO_LEmin), 30-min constant load tests to determine MLSS, a ramp test and verification bout for 𝑉˙O2max, and 20-min time trials for %𝑉˙O2_TT and PO_TT. Hypothesis 1 was tested via bivariate and partial correlations between %𝑉˙O2_MLSS and %𝑉˙O2_TT. Multiple regression models with 𝑉˙O2max, GE, ∆PO_LEmin, and %𝑉˙O2_MLSS (Hypothesis 2) or PO_MLSS instead of %𝑉˙O2_MLSS (Hypothesis 3), respectively, as predictors, and PO_TT as the dependent variable were used to test the hypotheses.
Results
%𝑉˙O2_MLSS was not correlated with %𝑉˙O2_TT (r = 0.17, p = 0.583). Neither %𝑉˙O2_MLSS (p = 0.424) nor PO_MLSS (p = 0.208) did improve the prediction of PO_TT in addition to 𝑉˙O2max and GE.
Conclusion
These results challenge the assumption that PO_MLSS or %𝑉˙O2_MLSS are independent predictors of supra-MLSS PO_TT and %V˙O2_TT.
... Some studies have investigated the relationship between FTP 20 and such physiological markers as the MLSS as well as other lactate threshold delineations, VO 2 max and the individual anaerobic threshold (Borszcz et al., 2018(Borszcz et al., , 2019Denham et al., 2020;Inglis et al., 2020;Jeffries et al., 2019;Lillo-Beviá et al., 2019;McGrath et al., 2019;Valenzuela et al., 2018). Others have also investigated the relationship between FTP 20 and performance prediction (Miller, 2014;Morgan et al., 2019;Sørensen et al., 2019). ...
... (3) Association with other power-related concepts (n = 6) (4) Performance prediction (n = 3) (Lillo-Beviá et al., 2019), one investigated the association with other physiological markers and performance prediction (Sørensen et al., 2019) and one investigated the association with other power-related concepts and performance prediction (Morgan et al., 2019). ...
... The authors of the FTP 20 test originally claimed that FTP could be used as a surrogate to the MLSS (Allen & Coggan, 2012). This scoping review identified three studies that included investigations into the relationship between FTP 20 and the MLSS (Borszcz et al., 2019;Inglis et al., 2020;Lillo-Beviá et al., 2019). Borszcz et al. (2019) concluded that for trained and welltrained cyclists (VO 2 max 62.3 (6.4) ml·kg −1 ·min −1 ), FTP 20 could be a reliable and practical alternative to the MLSS. ...
Functional Threshold Power (FTP) in cycling is increasingly used in exercise prescription, particularly with the rise in use of home trainers and virtual exercise platforms. FTP testing does not require biological sampling and is considered a more practical test than others. This scoping review investigated what is known about the 20-minute FTP (FTP²⁰) test. A three-step search strategy was used to identify studies in relevant databases (PubMed, CINAHL, SportDiscus, Google Scholar, Web of Science) and grey literature. Data were extracted and common themes identified which allowed for descriptive analysis and thematic summary. Fifteen studies were included. The primary focus fitted broadly into four themes: reliability, association with other physiological markers, other power-related concepts and performance prediction. The FTP²⁰ test was reported as a reliable test. Studies investigating the relationship of FTP²⁰ with other physiological markers and power-related concepts reported large limits of agreement suggesting parameters cannot be used interchangeably. Some findings indicate that FTP²⁰ may be useful in performance prediction. The majority of studies involved trained male cyclists. Overall, existing literature on the FTP²⁰ test is limited. Further investigation is needed to provide physiological justification for FTP²⁰ and inform use in exercise prescription in a range of populations.
... Allen and Cogan [16] set 95% of the MPO obtained in FTP20 as a predictive value for FTP 60 in cycling. Thereafter, a few studies [23][24][25] confirmed this 95% individual correction factor (ICF% = FTP 60 /FTP 20 ) between both TTs, whereas some others [26][27][28] found stronger associations between FTP 20 and MLSS subtracting~10% to the MPO achieved during the TT, instead of 5%. Furthermore, other TTs ranging from 3 to 30 min were proposed as MLSS predictors of FTP 60 [24,29]. ...
... The ICF% found was 93.6%, which contradicted the 95% established by Allen and Coggan [16] and supported others [23][24][25]. Contrary, recent studies [26][27][28] pointed that the well-accepted rule of subtracting 5% from the FTP 20 MPO is not a "one-size-fits-all" accurate method for FTP 60 estimation as it may differ depending on the athlete's level of performance. Our findings support this statement as an overestimating trend would affect our non-elite athletes when 95% of the FTP 20 is applied. ...
... Furthermore, Valenzuela et al. [24] tested two different cyclist groups (i.e., trained and recreational) and claimed that lower fitness status could result in FTP 60 overestimation as only the trained group matched the 95% adjustment for FTP20. Moreover, MacInnis described an ICF% of 90% for FTP 20 in 8 well-trained cyclists [39], whereas Lillo-Bevia tested 11 trained cyclist and triathletes finding an ICF% of 91% [27]. It should be considered that the aforementioned studies did not match the 95% adjustment [26][27][28] followed by a modified warm-up protocol (i.e., ≤15 min at self-selected pace), whereas those that reported a 95% correction between test [23][24][25] strictly followed the warm-up protocol originally proposed by Allen and Coggan [16] (50 min, including three 1-min accelerations and a 5-min all-out effort). ...
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests.
... supervision was assessed. The data revealed high test-retest reliability across 20-, 5-and 1-min PPO tests with low coefficients of variation and typical error of measurement (TEM) within 2%, in line with laboratory-based assessments of peak power output (MacInnis,Thomas and Phillips, 2018;Lillo-Bevia et al., 2019;McGrath et al., 2019;Borszcz, Tramontin and Costa, 2020). This reliability data, taken alongside the high ecological validity of the present study, reaffirms the validity of the data presented and the sensitivity of the current testing battery in detecting differences between time points and groups.Understanding the daily impact of training with reduced carbohydrate availability is essential for coaches, sport scientists and nutritionists alike.Hulston et al. (2010) andYeo et al. (2008b) have previously reported the effects of training with low carbohydrate availability on HIT session power output across a "train low" programme. ...
... MacInnis,Thomas and Phillips (2018) have previously investigated the reliability of repeated power tests of 4, 20 and 60 minutes in duration in trained cyclists showing high levels of reliability which were strongly associated with 60-min TT performance. More recent data have supported these observations in laboratory settings across a range of power durations(Lillo-Bevia et al., 2019;McGrath et al., 2019;Borszcz, Tramontin and Costa, 2020). To date, evidence suggests high level between-trial of reliability between PPO in laboratory-based studies utilising online gas analysis and blood lactate measures. ...
Endurance athletes have traditionally been advised to consume high carbohydrate intake before, during and after exercise to support high training loads and facilitate recovery. Accumulating evidence suggests periodically training with low carbohydrate availability, termed “train-low”, augments skeletal oxidative adaptations. Comparably, to account for increased carbohydrate utilisation during exercise in hot environmental conditions, nutritional guidelines advocate high carbohydrate intake. Recent evidence suggests heat stress induces oxidative adaptation in skeletal muscle, augmenting mitochondrial adaptation during endurance training. This thesis aimed to assess the efficacy of training with reduced carbohydrate and the impact of elevated ambient temperatures on performance and metabolism. Chapter 4 demonstrated 3 weeks of Sleep Low-Train Low (SL-TL) improves performance when prescribed and completed remotely. Chapter 5 implemented SL-TL in hot and temperate conditions, confirming SL-TL improves performance and substrate metabolism, whilst additional heat stress failed to enhance performance in hot and temperate conditions following the intervention. Chapters 6 and 7 optimised and implemented a novel in vitro skeletal muscle exercise model combining electrical pulse stimulation and heat stress. Metabolomics analysis revealed an ‘exercise-induced metabolic response, with no direct metabolomic impact of heat stress. Chapter 8 characterised the systemic metabolomic response to acute exercise in the heat following SL-TL and heat stress intervention revealing distinct metabolic signatures associated with exercise under heat stress.
In summary, this thesis provides data supporting the application of the SL-TL strategy during endurance training to augment adaptation. Data also highlights the impact of exercise, environmental temperature and substrate availability on skeletal muscle metabolism and the systemic metabolome. Together, these data provide practical support for the efficacy of the SL-TL strategy to improve performance and adaptation whilst casting doubt on the utility of this approach in hot environments in endurance-trained athletes.
... All-out time trials (TT), which target at completing fixed distances in min-imum time or generating maximum work in fixed time, do not need expensive equipment and trained staff and thus can be an attractive alternative for recreational athletes. In recent years, TTs experienced a revival under the term functional threshold power, which is the average workload during a prolonged all-out test (Allan & Coggan, 2010;Borszcz, Ferreira Tramontin, & Pereira Costa, 2019;Inglis, Ianetta, Passfield, & Murias, 2020;Jeffries, Simmons, Patterson, & Waldron, 2019;Lillo-Bevia et al., 2019;McGrath, Mahony, Fleming, Raleigh, & Donne, 2021). Capability of all-out TTs for predicting performance and estimating workload at AnT was proven in numerous investigations on experienced, well-trained cyclists (Bentley, McNaughton, Thompson, & Vleck, 2001;Burnley, Doust, & Vanhatlo, 2006;Campbell, Sousa, Ferreira, Assenço, & Simes, 2007;Groslambert et al., 2004;Harnish, Swensen, & Pate, 2001;Sperlich, Haegele, Thissen, Mester, & Holmberg, 2011;Swensen, Harnish, Beitman, & Keller, 1999). ...
... Accuracy of CPT approach in comparison to literature methods R 2 for the interrelationship between prolonged TTs and MLSS range from 0.71 to 0.99 (Borszcz et al., 2019;Campbell et al., 2007;Harnish et al., 2001;Inglis et al., 2020;Lillo-Bevia et al., 2019). R 2 for the interrelation between lactate threshold and MLSS range from 0.31 to 0.90 and from 'not significant' to 0.95 for ventilatory threshold and MLSS (Figueira, Caputo, Pelarigo, & Denadai, 2008;Hauser, Adam, & Schulz, 2014;Heck, 1990;Laplaud, Guinot, Favre-Juvin, & Flore, 2006;MacIntosh, Esau, & Svedahl, 2002;Pallares, Moran-Navarro, Ortega, Fernandez-Elias, & Mora-Rodriguez, 2016;Peinado et al., 2016;Smekal et al., 2012;Van Schuylenbergh, Vanden Eynde, & Hespel, 2004;Zwingmann et al., 2019). ...
Introduction
Prolonged time trials proved capable of precisely estimating anaerobic threshold. However, time trial studies in recreational cyclists are missing. The aim of the present study was to evaluate accuracy and viability of constant power threshold, which is the highest power output constantly maintainable over time, for estimating maximal lactate steady state in recreational athletes.
Methods
A total of 25 recreational athletes participated in the study of whom 22 (11 female, 11 male) conducted all constant load time trials required for determining constant power threshold 30 min and 45 min, which is the highest power output constantly maintainable over 30 min and 45 min, respectively. Maximal lactate steady state was assessed subsequently from blood samples taken every 5 min during the time trials.
Results
Constant power threshold over 45 min (175.5 ± 49.6 W) almost matched power output at maximal lactate steady state (176.4 ± 50.5 W), whereas constant power threshold over 30 min (181.4 ± 51.4 W) was marginally higher ( P = 0.007, d = 0.74). Interrelations between maximal lactate steady state and constant power threshold 30 min and constant power threshold 45 min were very close (R ² = 0.99, SEE = 8.9 W, Percentage SEE (%SEE) = 5.1%, P < 0.001 and R ² = 0.99, SEE = 10.0 W, %SEE = 5.7%, P < 0.001, respectively).
Conclusions
Determination of constant power threshold is a straining but viable and precise alternative for recreational cyclists to estimate power output at maximal lactate steady state and thus maximal sustainable oxidative metabolic rate.
... These results would, overall, support that the mean PO during a 20-min test might be reflective of the RCP, and that 95% of that PO might be similar to the MLSS. However, a recent study reported that although the FTP was strongly correlated to the MLSS (r = 0.95), the former corresponded to a significantly higher PO [27]. Notably, we recently found that the mean PO obtained during a 20-min test strongly correlated with the RCP in highly-trained cyclists, but significantly higher values were found for the PO at the RCP compared to the PO that was sustainable during the 20 min (bias ~12%) [28]. ...
... Notably, we recently found that the mean PO obtained during a 20-min test strongly correlated with the RCP in highly-trained cyclists, but significantly higher values were found for the PO at the RCP compared to the PO that was sustainable during the 20 min (bias ~12%) [28]. Lillo-Beviá et al. also reported higher PO values at the RCP than those obtained during a 20-min test [27]. These findings should be confirmed in future studies. ...
The time to exhaustion (tlim) at the respiratory compensation point (RCP) and whether a physiological steady state is observed at this workload remains unknown. Thus, this study analyzed tlim at the power output eliciting the RCP (tlim at RCP), the oxygen uptake (VO2) response to this effort, and the influence of endurance fitness. Sixty male recreational cyclists (peak oxygen uptake [VO2peak] 40-60 mL•kg•min −1) performed an incremental test to determine the RCP, VO2peak, and maximal aerobic power (MAP). They also performed constant-load tests to determine the tlim at RCP and tlim at MAP. Participants were divided based on their VO2peak into a low-performance group (LP, n = 30) and a high-performance group (HP, n = 30). The tlim at RCP averaged 20 min 32 s ± 5 min 42 s, with a high between-subject variability (coefficient of variation 28%) but with no differences between groups (p = 0.788, effect size = 0.06). No consistent relationships were found between the tlim at RCP and the different fitness markers analyzed (RCP, power output (PO) at RCP, VO2peak, MAP, or tlim at MAP; all p > 0.05). VO2 remained steady overall during the tlim test, although a VO2 slow component (i.e., an increase in VO2 >200 mL•min −1 from the third min to the end of the tests) was present in 33% and 40% of the participants in HP and LP, respectively. In summary, the PO at RCP could be maintained for about 20 min. However, there was a high between-subject variability in both the tlim and in the VO2 response to this effort that seemed to be independent of fitness level, which raises concerns on the suitability of this test for fitness assessment.
... The KICKR trainer was set to open test mode during the TT, allowing participants to change gears and intensity freely throughout. The participants were instructed to produce their maximal power output for the TT, adopt their personal pacing strategies [36][37][38], and to complete the total distance in the fastest time possible [33]. Participants were permitted to drink water as needed, select their own music, and listen to the same playlist during each visit. ...
Background: Quercetin (QCT) and citrulline (CIT) have been independently associated with improved antioxidant capacity and nitric oxide (NO) production, potentially enhancing cardiovascular function and exercise performance. This study aimed to evaluate the combined and independent effects of QCT and CIT supplementation on NO metabolites and antioxidant biomarkers in 50 trained cyclists undergoing a 20 km cycling time trial (TT). Methods: In a randomized, double-blind, placebo-controlled design, forty-two male and eight female trained cyclists were assigned to QCT + CIT, QCT, CIT, or placebo (PL) groups. Supplements were consumed twice daily for 28 days. Biochemical assessments included NO metabolites (nitrate/nitrite), ferric reducing antioxidant power (FRAP), superoxide dismutase (SOD) activity, and antioxidant capacity, measured pre- and post-TT. Results: NO metabolites were significantly elevated post-supplementation (p = 0.03); however, no significant interaction effects were observed for NO metabolites, FRAP, SOD, or antioxidant capacity across the groups (p > 0.05). Post-hoc analyses revealed that QCT significantly reduced FRAP concentrations compared to PL (p = 0.01), while no significant changes in SOD or antioxidant capacity were found across any groups. Conclusions: These findings suggest that combined and independent QCT and CIT supplementation did not significantly improve these biomarkers, suggesting that baseline training adaptations, supplementation timing, and individual variability may influence the efficacy of these compounds in enhancing exercise performance and oxidative stress markers. The ergogenic efficacy of QCT + CIT on antioxidant-related markers remains inconclusive.
... In light of the significant correlation between the FTP test and cycling performance outcomes, a critical evaluation of the protocol employed, and the test's reliability is warranted. Regarding the reliability, the FTP values assessed by a 20 min test have been reported as both reliable and repeatable in several previous researchers [27][28][29]. Considering the test prtocol, the warm up originally prescribed by Allen and Coggan is 45 min, including 20 min at self-selected low intensity, 3 × 1 min of fast pedalling accelerations, 5 min at self-selected low intensity, 5 min at maximal effort Content courtesy of Springer Nature, terms of use apply. ...
Purpose
Previous study has shown that cycling is the most predictive modality in the Ironman 70.3 triathlon distance. As a result, understanding the physiological and anthropometric variables that are mostly closely related to cycling performance can help coaches and athletes to direct their training programs. This study aimed to investigate the physiological, anthropometric, and general training characteristics influencing overall race time and cycling split time in Ironman 70.3. The present study also investigated the significance of body composition as a performance-related variable.
Methods
A questionnaire was used to assess training characteristics in 12 athletes (six men and six women), body composition in dual X-ray absorptiometry, and physiological variables in an incremental cardiopulmonary test. Ironman 70.3 São Paulo–Brazil 2023 was completed by all participants. The relationship between performance and the variables measured were investigated, and a multiple regression model for cycling split time and overall race time was developed.
Results
Functional threshold power (FTP) can predict cycling split time in Ironman 70.3 (r ² = 0.638, p = 0.002). Maximal oxygen uptake ( V ˙ O 2 max) (r ² = 0.667, p = 0.001) can predict overall race time. FTP and V ˙ O 2 max are also strongly related to lean mass and fat mass percentage.
Conclusion
While FTP is the most important predictor of cycling split time, V ˙ O 2 max is the most important predictor of overall race time in an Ironman 70.3. Furthermore, because body composition (fat mass %) and muscle mass (kg) are variables strongly related to FTP and V ˙ O 2 max, we recommend that coaches and athletes consider to conduct a body composition assessment.
... The KICKR Trainer (Wahoo Fitness, Atlanta, Georgia) was set in open test mode during the TT, allowing participants to change gears and intensity freely. The participants were asked to produce their maximal power output for the TT and adopt their personal pacing strategies [48][49][50] and were instructed to complete the total distance in the fastest time possible [45]. Participants were allowed to drink water ad libitum and were allowed to listen to the same playlist of music at each visit. ...
Background:
There is growing interest in the use of nutrition and dietary supplements to optimize training and time-trial (TT) performance in cyclists. Separately, quercetin (QCT) and citrulline (CIT) have been used as ergogenic aids to improve oxygen (VO2) kinetics, perceived effort, and cycling TT performance. However, whether the combination of QCT and CIT can provide additive benefits and further enhance cycling performance production is currently unknown.
Methods:
We examined 28-days of QCT + CIT supplementation on TT performance and several performance measures (i.e. mean power, VO2, respiratory exchange ratio (RER), and rate of perceived exertion (RPE)). Forty-eight highly trained cyclists were assigned to one of four supplementation groups: (1) QCT + CIT (QCT: 500 mg, CIT: 3000 g), (2) QCT (500 mg), (3) CIT (3000 mg), or (4) placebo (3500 mg of a zero-calorie flavored crystal light package). Supplements were consumed two times per day for 28 consecutive days. Participants performed a 20-km cycling time-trial race, pre- and post-supplementation to determine the impact of the combined effects of QCT + CIT.
Results:
There were no potential benefits of QCT +CIT supplementation on TT performance and several performance measures. However, there was an improvement in VO2 from pre-to-post-supplementation in QCT (p = 0.05) and CIT (p = 0.04) groups, but not in the QCT+CIT and PL groups.
Conclusions:
QCT + CIT does not seem beneficial for 20-km TT performance; further exploration with a focus on an increase in cycling duration or QCT+CIT combined with additional polyphenols may amplify any perceived bioactive or metabolic effects on cycling performance. The efficacy of QCT + CIT supplementation to improve cycling performance remains ambiguous.
... , Burnley, Black, Poole, & Vanhatalo (2019) for CP and Allen & Coggan (2012) for FTP-. Hence, differences with respect to the MLSS intensity are sometimes reported (Galán-Rioja, González-Mohíno,Poole, & González- Ravé, 2020;Jones et al., 2019;Lillo-Beviá et al., 2022). In our opinion, rather than arguing about whether CP or FTP, the important question is to know how their values are obtained and to be consistent in their assessment. ...
In recent years, the number of scientific publications related to sports performance, and in particular cycling performance, has increased exponentially. Several authors want to make their contribution to scientific advances. However, it should be questioned whether all these contributions really represent progress. In many cases, their contribution is simply a different way of referring to the same concept. For example, nowadays it seems that referring to aerobic and anaerobic metabolism is a classic terminology of the past, with the terms oxidative and glycolytic being more appropriate. Without going into the details, the underlying concept is similar. Rather than putting one label or the other, the important point should be to understand, in each case, the predominant way in which energy is obtained. However, on the other hand, it is still common to use concepts that have been known to be erroneous for years. A clear example is the role of lactate: among many other functions, it is an essential metabolite in energy production and in the reduction of acidosis (Robergs, 2011). Despite this, it is still common to observe many professionals mistakenly claiming that its accumulation in the body is the cause of metabolic acidosis.
In cycling, and particularly since the appearance of power meters, several metrics have been developed for the monitoring and control of training. This has led to the existence of different terms to refer to the same concept. Worst of all is that, sometimes, some coaches –or pseudo-coaches– tend to generate noise and confusion by using a large number of metrics, without being aware –or intentionally so– that, in some cases, the terms used refer to the same concept. Therefore, table 1 shows metrics whose underlying concept is the same or similar, related to those used by two of the most widely used software in cycling: Golden Cheetah and Training Peaks/WKO5.
Table 1. Equivalences of some metrics used in Training Peaks/WKO and Golden Cheetah.
Training Peaks/WKO5
Golden Cheetah
Functional Threshold Power (FTP)
Critical Power (CP)
Functional Reserve Capacity (FRC)
W'
Normalized Power (NP)
IsoPower / xPower
Intensity Factor (IF)
BikeIntensity / Relative intensity
Training Stress Score® (TSS)
BikeScore / BikeStress
Acute Training Load (ATL)
Short Term Stress (STS)
Chronic Training Load (CTL)
Long Term Stress (LTS)
Training Stress Balance (TSB)
Stress Balance (SB)
Of all these metrics, we will briefly describe similarities and differences of four of them. Specifically, reference will be made to 1) Functional Threshold Power (FTP) vs Critical Power (CP); 2) Functional Reserve Capacity (FRC) vs W’; 3) Normalized Power (NP) vs xPower; and 4) Training Stress Score® vs BikeScore.
Functional Threshold Power and Critical Power
Both FTP and CP are metrics that aim to provide a sustainable intensity over time without fatigue. That is, FTP and CP relate to the transition between a steady-state and non-steady-state oxidative metabolism (Barranco-Gil et al., 2020), or between the heavy and severe domains (Poole, Burnley, Vanhatalo, Rossiter, & Jones, 2016). In other words, they should estimate the maximal lactate steady state (MLSS) intensity (Borszcz, Tramontin, & Costa, 2019). However, FTP and CP give different values (Karsten et al., 2021). This is because they are obtained from different tests –see Jones, Burnley, Black, Poole, & Vanhatalo (2019) for CP and Allen & Coggan (2012) for FTP–. Hence, differences with respect to the MLSS intensity are sometimes reported (Galán-Rioja, González-Mohíno, Poole, & González-Ravé, 2020; Jones et al., 2019; Lillo-Beviá et al., 2022). In our opinion, rather than arguing about whether CP or FTP, the important question is to know how their values are obtained and to be consistent in their assessment. Both tests are simpler and more practical alternatives to the traditional method of determining MLSS.
Functional Reserve Capacity and W’
When working above FTP or CP, much of the energy comes from anaerobic –or phosphagen and glycolytic– metabolism. The energy that can be obtained by this route is limited, which is why the concept of anaerobic energy reserve or anaerobic work capacity is proposed. This existing concept is what has been coined as W' or FRC. That is, the amount of work that can be done above CP or FTP, respectively. The two terms are therefore equivalent. Their differences lie exclusively in the measurement of CP or FTP.
Normalized Power and xPower
The NP proposed by Coggan and the xPower proposed by Skiba are virtually identical 4-step mathematical algorithms that aim to estimate the average power that could have been maintained constant for the physiological cost incurred. The only difference between both algorithms lies in the first step: Coggan proposes a 30-second moving average, and Skiba modifies it performing a 25-second exponentially weighted moving average, considering that it better represents the physiological delay of the organism –see Clarke & Skiba (2013) for xPower algorithm and Allen & Coggan (2012) for NP algorithm–.
Training Stress Score® and BikeScore
BikeScore and TSS are two quantification indexes whose formula is identical, except that the former uses xPower and CP, and the latter NP and FTP in its calculations (see equations 1 and 2 for BikeScore and TSS, respectively). In both cases, an effort of 1 hour at CP or FTP would give a value of 100 points.
In essence, rather than using a large number of metrics, the important question is to know what they mean, as many of them are equivalent or very similar. Another matter is that there is some hidden interest in using a lot of terms in order to generate noise and confuse athletes. Perhaps, some coaches prefer to look like “sophisticated” scientist rather than being better understood by athletes, at the time some brands or authors create new terms for old and well defined concepts.
... Alternativ könnte in einem Belastungsbereich mit sehr hohen Intensitäten auch über das Belastungsempfinden oder über die maximale Herzfrequenz gesteuert werden (RPE: 6-20, ≥ 14; HRMAX: ≥ 77%, vgl. Garber et al., 2011Garber et al., , S. 1341, "vigorous -near-maximal to maximal") oder über etablierte Konzepte einer anaeroben Schwelle (Meyer et al., 2005;Binder et al., 2008;Beneke et al., 2011;Hofmann & Tschakert, 2017;Lillo-Beviá et al., 2019). Wenn weitere Validierungsstudien bestätigen, dass ein spezifischer Wert von DFA-alpha1 mit dem Übergang an der aeroben Schwelle auch bei anderen Probandenkollektiven in Verbindung steht, könnte das Monitoring von DFA-alpha1 in Echtzeit während Akutbelastung sowohl für Athleten als auch für Trainer sehr nützlich für die Trainingssteuerung sein. ...
Vorgestellt und diskutiert wird der Ansatz der nicht-linearen Zeitreihenanalyse der Herzfrequenzvariabilität (HRV) mit Hilfe des Kurzzeitskalierungsexponenten alpha1 der trendbereinigten Fluktuationsanalyse (engl.: Detrended Fluctuation Analysis, DFA-alpha1) während ausdauer-akzentuierter Akutbelastung. Hierfür wird der Forschungsstand zur nicht-linearen Analyse der HRV mit Hilfe von DFA-alpha1 während verschiedener Belastungscharakteristika von ausdauer-akzentuierten Belastungen in Labor- und Feldstudien dargestellt. Es wird zudem konkretisiert, inwieweit DFA-alpha1 als “systemischer Globalparameter“ und Proxy für die organismische Beanspruchung und Regulation dienen kann und konsistent nicht-redundante Informationen zur Herzfrequenzregulation während ausdauer-akzentuierten Belastungen im Vergleich zu Zeit- und Frequenzbereichsparametern der HRV liefert. Perspektivisch wird die Anwendung von DFA-alpha1 als systemischer Parameter zur Schwellenbestimmung eines unteren Intensitätsbereichs für das Training bei ausdauer-akzentuierten Belastungen diskutiert. Hierbei kann nach der vorliegenden Datenlage eine Trainingssteuerung hinsichtlich einer Schwelle für niedrige Intensitäten (äquivalent im Bereich der ersten ventilatorischen Schwelle) anhand eines Regulationsbereichs zwischen einer selbstähnlichen (fraktalen) Zeitreihe der HRV mit hoher Komplexität (DFA-alpha1: 1,0) und einer vorwiegend zufälligen Regulationsdynamik in der Zeitreihe mit geringer Komplexität (DFA-alpha1: 0,5) erfolgen und ein Übergangsbereich bei ca. 0,75 festgelegt werden. Dieser Übergang erfolgt zwischen den zwei organismischen Zuständen der (1) Integration und Synchronisation von Subsystemen bei geringer Belastungsintensität sowie der (2) progressiven Segregation und Mechanisierung von Subsystemen bei hoher Belastungsintensität. Trotz der organismisch begründbaren Anwendung dieses Übergangsbereichs organismischer Regulationszustände unter Ruhebedingungen und der vielversprechenden Datenlage während ausdauer-akzentuierter Akutbelastung bedarf es weiterer Studien, um die konkrete Bedeutung für das Überschreiten einer niedrigen Intensitätsschwelle bei DFA-alpha1 von 0,75 für die Trainingspraxis zu evaluieren und in den Kontext anderer etablierter Schwellenkonzepte einzuordnen. Nicht zuletzt gilt es zukünftig Perspektiven für eine konkrete Software-Implementierung in Herzfrequenz- bzw. HRV-Messgeräten zu verdeutlichen, um das dargestellte Vor-gehen konkret für die Trainingspraxis als Real-Time-Monitoring-Ansatz anwendbar zu machen.
... Despite its widespread use in professional and amateur cyclists, there is incomplete agreement on the relationships between FTP and traditional exercise intensity boundaries (21). Moreover, although it was intended to be a field test, studies on the physiological underpinnings of FTP were mostly confined to the laboratory setting, where mixed agreement was found with a 60-minute TT (3,20,23), as well as the ventilatory compensation point (2,33), the individual anaerobic threshold (3,20), the Dmax lactate threshold (23,32,35), the 4 mM (16,32) lactate threshold (P 4mM ), the maximal lactate steady state (4,15,19), and the critical power (CP) (17,24,27), with most studies refuting interchangeability. The 20-minute TT naturally evokes the concept of PO-duration (T lim ) relationship (18), its simplest hyperbolic form being T lim 5 W9/(PO-CP), where the curvature constant W9 (the amount of work that can be performed above CP) interestingly resulted unrelated to FTP (27). ...
Vinetti, G, Rossi, H, Bruseghini, P, Corti, M, Ferretti, G, Piva, S, Taboni, A, and Fagoni, N. The functional threshold power field test exceeds laboratory performance in junior road cyclists. J Strength Cond Res 37(9): 1815–1820, 2023—The functional threshold power (FTP) field test is appealing for junior cyclists, but it was never investigated in this age category, and even in adults, there are few data on FTP collected in field conditions. Nine male junior road cyclists (16.9 ± 0.8 years) performed laboratory determination of maximal aerobic power (MAP), 4-mM lactate threshold (P4mM), critical power (CP), and the curvature constant (W′), plus a field determination of FTP as 95% of the average power output during a 20-minute time trial in an uphill road. The level of significance was set at p < 0.05. Outdoor FTP (269 ± 34 W) was significantly higher than CP (236 ± 24 W) and P4mM (233 ± 23 W). The V˙O2peak of the field FTP test (66.9 ± 4.4 ml·kg⁻¹·min⁻¹) was significantly higher than the V˙O2peak assessed in the laboratory (62.7 ± 3.7 ml·kg⁻¹·min⁻¹). Functional threshold power was correlated, in descending order, with MAP (r = 0.95), P4mM (r = 0.94), outdoor and indoor V˙O2peak (r = 0.93 and 0.93, respectively), CP (r = 0.84), and W′ (r = 0.66). It follows that in junior road cyclists, the FTP field test was feasible and related primarily to aerobic endurance parameters and secondarily, but notably, to W′. However, the FTP field test significantly exceeded all laboratory performance tests. When translating laboratory results to outdoor uphill conditions, coaches and sport scientists should consider this discrepancy, which may be particularly enhanced in this cycling age category.
... Investigators have suggested MLSS could be used interchangeably with FTP (3). However, the MLSS marker is not frequently used in the field because of the number of discrete test sessions required (12), a problem perhaps overcome by using the FTP test. The graded exercise test (GxT) has maintained a prevalent position for the assessment of aerobic exercise fitness. ...
Functional Threshold Power (FTP) is a validated index of a maximal quasi steady-state cycling intensity. The central component of the FTP test is a maximal 20-min time-trial effort. A model to predict FTP from a cycling graded exercise test (m-FTP) was published that estimated FTP without the requirement of the exhaustive 20-min time-trial. The predictive model (m-FTP) was trained (developed to find the best combination of weights and bias) on a homogenous group of highly-trained cyclists and triathletes. This investigation appraised the external validity of the m-FTP model vis-à-vis the alternate modality of rowing. The reported m-FTP equation purports to be sensitive to both changing levels of fitness, and exercise capacity. To assess this claim, eighteen (7 female, 11 male) heterogeneously-conditioned rowers were recruited from regional rowing clubs. The first rowing test was a 3-min graded incremental test with a 1-min break between increments. The second test was a rowing adapted FTP test. There were no significant differences between rowing FTP (r-FTP) and m-FTP (230 ± 64 versus 233 ± 60 W, respectively, F = 1.13, P = 0.80). Computed Bland-Altman 95% LoA between r-FTP and m-FTP were (-18 W to + 15 W), sy.x was 7 W, and 95 %CI of regression were 0.97 to 0.99. The r-FTP equation was demonstrated to be effective in predicting a rowers 20-min maximum power; further appraisal of the physiological response to rowing for 60-min at the corresponding calculated FTP requires investigation.
... A plausible approach to make the index adjustable could be the use of the theoretical available time to exhaustion (TTE) at different cycling physiological events, namely maximal oxygen uptake (VO 2max ), respiratory compensation point (RCP), MLSS, and first ventilatory threshold (VT1) [38,[41][42][43][44][45][46][47]. It has also been suggested to use the hyperbolic energy production curve for different times of efforts in order to contemplate changes in energy production during the exercise session [48]. ...
Workload is calculated from exercise volume and intensity. In endurance sports, intensity has been measured using heart rate or RPE, giving rise to load indexes such as sRPE or TRIMP. In cycling, the advent of power meters led to new indexes, such as TSS. All these indexes have limitations, especially for high intensity exercise. Therefore, a new index for cycling is proposed, the Power Profile Index (PPi), which includes a weighting factor obtained from the relative exercise intensity and stage type. Using power data from 67 WorldTour cyclists and fatigue records in different stage types from 102 road cyclists, weighting factors for intensity and stage type were determined. Subsequently, the PPi was computed and compared to current indexes using data from a WorldTour team during the 2018 Tour de France. The proposed index showed a strong correlation with perceived fatigue as a function of stage type (R² = 0.9996), as well as no differences in the load quantification in different types of stage profiles (p = 0.292), something that does not occur with other indexes such as TSS, RPE, or eTRIMP (p < 0.001). Therefore, PPi is a new index capable of quantifying the high intensity efforts that produce greater fatigue, as well as considering the stage type.
... Due to the expansion of power meters through reduced cost and improvements in their reproducibility [50], the implementation of power-based training prescription has become increasingly popular in cyclists over the last several years. Using this approach, coaches can consult, analyse, and monitor a range of physiological (HR, power, pace/speed, energy expenditure) and perceptual (RPE, overall feeling and wellness) training metrics for multiple athletes simultaneously [60][61][62][63]. Additionally, this approach allowed us to recruit a large number of participants (55 in our study compared to 21 and 13 in Marquet et al. [23] and Riis et al. [24] who used a similar study design, albeit laboratory based, respectively), and analyse day-to-day responses to the "sleep low-train low" intervention for the first time. ...
Background
“Sleep Low-Train Low” is a training-nutrition strategy intended to purposefully reduce muscle glycogen availability around specific exercise sessions, potentially amplifying the training stimulus via augmented cell signalling. The aim of this study was to assess the feasibility of a 3-week home-based “sleep low-train low” programme and its effects on cycling performance in trained athletes.
Methods
Fifty-five trained athletes (Functional Threshold Power [FTP]: 258 ± 52W) completed a home-based cycling training program consisting of evening high-intensity training (6 × 5 min at 105% FTP), followed by low-intensity training (1 hr at 75% FTP) the next morning, three times weekly for three consecutive weeks. Participant’s daily carbohydrate (CHO) intake (6 g·kg⁻¹·d⁻¹) was matched but timed differently to manipulate CHO availability around exercise: no CHO consumption post- HIT until post-LIT sessions [Sleep Low (SL), n = 28] or CHO consumption evenly distributed throughout the day [Control (CON), n = 27]. Sessions were monitored remotely via power data uploaded to an online training platform, with performance tests conducted pre-, post-intervention.
Results
LIT exercise intensity reduced by 3% across week 1, 3 and 2% in week 2 (P < 0.01) with elevated RPE in SL vs. CON (P < 0.01). SL enhanced FTP by +5.5% vs. +1.2% in CON (P < 0.01). Comparable increases in 5-min peak power output (PPO) were observed between groups (P < 0.01) with +2.3% and +2.7% in SL and CON, respectively (P = 0.77). SL 1-min PPO was unchanged (+0.8%) whilst CON improved by +3.9% (P = 0.0144).
Conclusion
Despite reduced relative training intensity, our data demonstrate short-term “sleep low-train low” intervention improves FTP compared with typically “normal” CHO availability during exercise. Importantly, training was completed unsupervised at home (during the COVID-19 pandemic), thus demonstrating the feasibility of completing a “sleep low-train low” protocol under non-laboratory conditions.
... For example, power metres mounted on the bicycle or in the pedals enable real-time measurement of power output. [41][42][43] These data, along with other metrics such as heart rate and GPS, 44 can be used to generate a range of summary measures of training load, such as the Training Stress Score (TSS), 11 and the Training Impulse (TRIMP). 45 46 These are commonly used by cyclists and coaches to analyse and plan training and competition and have the potential to be used in studies investigating the relationship between cycling loads and injury risk. ...
In 2020, the IOC released a consensus statement that provides overall guidelines for the recording and reporting of epidemiological data on injury and illness in sport. Some aspects of this statement need to be further specified on a sport-by-sport basis. To extend the IOC consensus statement on methods for recording and reporting of epidemiological data on injury and illness in sports and to meet the sport-specific requirements of all cycling disciplines regulated by the Union Cycliste Internationale (UCI). A panel of 20 experts, all with experience in cycling or cycling medicine, participated in the drafting of this cycling-specific extension of the IOC consensus statement. In preparation, panel members were sent the IOC consensus statement, the first draft of this manuscript and a list of topics to be discussed. The expert panel met in July 2020 for a 1-day video conference to discuss the manuscript and specific topics. The final manuscript was developed in an iterative process involving all panel members. This paper extends the IOC consensus statement to provide cycling-specific recommendations on health problem definitions, mode of onset, injury mechanisms and circumstances, diagnosis classifications, exposure, study population characteristics and data collection methods. Recommendations apply to all UCI cycling disciplines, for both able-bodied cyclists and para-cyclists. The recommendations presented in this consensus statement will improve the consistency and accuracy of future epidemiological studies of injury and illness in cycling.
... In an effort to identify the AnT noninvasively, field-based tests have been devised with the functional threshold power (FTP) evaluation being a popular example [10]. However, results have been shown to vary depending on warmup protocol and test procedures [11][12][13]. In addition, the FTP is dependent on motivation, individual pacing strategy [14] and by its definition, physically exhausting. ...
Past attempts to define an anaerobic threshold (AnT) have relied upon gas exchange kinetics, lactate testing and field-based evaluations. DFA a1, an index of heart rate (HR) variability (HRV) fractal correlation properties, has been shown to decrease with exercise intensity. The intent of this study is to investigate whether the AnT derived from gas exchange is associated with the transition from a correlated to uncorrelated random HRV pattern signified by a DFA a1 value of 0.5. HRV and gas exchange data were obtained from 15 participants during an incremental treadmill run. Comparison of the HR reached at the second ventilatory threshold (VT2) was made to the HR reached at a DFA a1 value of 0.5 (HRVT2). Based on Bland–Altman analysis and linear regression, there was strong agreement between VT2 and HRVT2 measured by HR (r = 0.78, p < 0.001). Mean VT2 was reached at a HR of 174 (±12) bpm compared to mean HRVT2 at a HR of 171 (±16) bpm. In summary, the HR associated with a DFA a1 value of 0.5 on an incremental treadmill ramp was closely related to that of the HR at the VT2 derived from gas exchange analysis. A distinct numerical value of DFA a1 representing an uncorrelated, random interbeat pattern appears to be associated with the VT2 and shows potential as a noninvasive marker for training intensity distribution and performance status.
... In the 20-min FTP test, the athlete performs 20 min of all-out exercise and FTP is estimated as 95% of the average power achieved (FTP 20 ) [53]. Recent studies have compared FTP 20 with laboratoryderived MMSS estimates, with largely poor agreement shown [54][55][56][57][58][59]. For example, a recent study observed no difference between FTP and LT (using the Dmax method) in trained cyclists (17.6 ± 5.7 h week −1 ), whereas in recreational cyclists (7.06 ± 1.8 h week −1 ) FTP was substantially below the identified LT (2.93 ± 0.22 vs. 3.14 ± 0.18 W kg −1 , P < 0.05) [61]. ...
Profiling physiological attributes is an important role for applied exercise physiologists working with endurance athletes. These attributes are typically assessed in well-rested athletes. However, as has been demonstrated in the literature and supported by field data presented here, the attributes measured during routine physiological-profiling assessments are not static, but change over time during prolonged exercise. If not accounted for, shifts in these physiological attributes during prolonged exercise have implications for the accuracy of their use in intensity regulation during prolonged training sessions or competitions, quantifying training adaptations, training-load programming and monitoring, and the prediction of exercise performance. In this review, we argue that current models used in the routine physiological profiling of endurance athletes do not account for these shifts. Therefore, applied exercise physiologists working with endurance athletes would benefit from development of physiological-profiling models that account for shifts in physiological-profiling variables during prolonged exercise and quantify the ‘durability’ of individual athletes, here defined as the time of onset and magnitude of deterioration in physiological-profiling characteristics over time during prolonged exercise. We propose directions for future research and applied practice that may enable better understanding of athlete durability.
... The adoption of FTP as the de facto standard for performance measurement and tracking including amongst professional cyclists [121] has led to recent investigations into its physiological basis. As a measure of endurance performance FTP correlates strongly against other such endurance measures [122][123][124][125][126][127][128], but was not found to be an interchangeable or surrogate measure of lactate threshold [122,125,126,129,130], CP [131], respiratory compensation point [132], or MLSS [124,133] (see Table 2). This is unsurprising given that FTP is a measure of performance over an arbitrary chosen one-hour duration, which sits unquestionably within the confines of the heavy intensity domain, and as such does not align to any known physiological markers, thresholds or boundaries which define such laboratory-derived measurements [13]. ...
The two-parameter critical power (CP) model is a robust mathematical interpretation of the power–duration relationship, with CP being the rate associated with the maximal aerobic steady state, and W′ the fixed amount of tolerable work above CP available without any recovery. The aim of this narrative review is to describe the CP concept and the methodologies used to assess it, and to summarize the research applying it to intermittent cycle training techniques. CP and W′ are traditionally assessed using a number of constant work rate cycling tests spread over several days. Alternatively, both the 3-min all-out and ramp all-out protocols provide valid measurements of CP and W′ from a single test, thereby enhancing their suitability to athletes and likely reducing errors associated with the assumptions of the CP model. As CP represents the physiological landmark that is the boundary between heavy and severe intensity domains, it presents several advantages over the de facto arbitrarily defined functional threshold power as the basis for cycle training prescription at intensities up to CP. For intensities above CP, precise prescription is not possible based solely on aerobic measures; however, the addition of the W′ parameter does facilitate the prescription of individualized training intensities and durations within the severe intensity domain. Modelling of W′ reconstitution extends this application, although more research is needed to identify the individual parameters that govern W′ reconstitution rates and their kinetics.
... Given this restricted dynamic range at high work rates, it is not ideally suited as a measure of a high intensity threshold. Alternate methods for zone 2 to zone 3 transition are available including measurement of the RCP/LT2 by means of gas exchange, lactate testing or simply by functional threshold power (FTP) interval testing (Meyer et al., 2005;Binder et al., 2008;Beneke et al., 2011;Hofmann and Tschakert, 2017;Lillo-Beviá et al., 2019). Besides the possible limitations stated by Gronwald et al. (2019c) and Silva et al. (2017) regarding an unclear detailed physiologic interpretation of DFA-alpha1 and a possible influence of spontaneous breathing during exercise enabling physiologic coupling processes, some caution in HRV analysis interpretation during moderate to high intensity exercise may be needed if artifacts are present (Rincon Soler et al., 2018). ...
Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a “global” system parameter and proxy for organismic demands.
The 8-minute time trial (TT) is a methodological alternative to the 60-minute TT for evaluating the Functional Threshold Power (FTP) of cyclists, however, studies that tested its validity were not found in the researched literature. Therefore, research aims to assess the validity of the 8-minute TT. The study included 9 trained male cyclists, aged between 25.46 ± 7.49 years, who were assessed on three different days. On the first day, we measured personal data, anthropometrics, ventilatory thresholds and peak oxygen consumption. On the other days, we submitted the volunteers to the 8-and 60-minute TT. We analyzed the agreement between the procedures using the intraclass correlation coefficient (ICC) and its validity by Bland-Altman. We adopted a significance level of 5%, and we performed all analyses using the SPSS. The results suggest great agreement, especially between the second 8-minute stimulus and the reference test, for FTP (ICC: 0.792, p= 0.016), Wats per kilogram (ICC: 0.952, p< 0.001), Wats per kilogram of lean mass (ICC: 0.912, p= 0.001) and peak oxygen consumption (ICC: 0.882, p= 0.001). In addition, in all these variables, the volunteers were within the mean ± two standard deviations, as verified by the Bland-Altman plots. These results demonstrate the validity of the 8-minute TT, with more robust data being observed by the second stimulus of this protocol. RESUMO O contrarrelógio de 8 minutos (TT) é uma alternativa metodológica ao TT de 60 minutos para avaliar a Potência de Limiar Funcional (FTP) de ciclistas, no entanto, estudos que testaram sua validade não foram encontrados na literatura pesquisada. Portanto, a pesquisa visa avaliar a validade do TT de 8 minutos. O estudo incluiu 9 ciclistas do sexo masculino treinados, com idade entre 25,46 ± 7,49 anos, que foram avaliados em três dias diferentes. No primeiro dia, medimos dados pessoais, antropometria, limiares de ventilação e pico de consumo de oxigênio. Nos outros dias, submetemos os voluntários ao TT de 8 e 3 CUADERNOS DE EDUCACIÓN Y DESARROLLO, Portugal, v.16, n.3, p. 01-23, 2024 60 minutos. Analisamos a concordância entre os procedimentos utilizando o coeficiente de correlação intraclasse (ICC) e sua validade por Bland-Altman. Adotamos um nível de significância de 5% e realizamos todas as análises utilizando o SPSS. Os resultados sugerem grande concordância, especialmente entre o segundo estímulo de 8 minutos e o teste de referência, para FTP (ICC: 0,792, p= 0,016), Wats por quilograma (ICC: 0,952, p< 0,001), Wats por quilograma de massa magra (ICC: 0,912, p= 0,001) e consumo máximo de oxigênio (ICC: 0,882, p= 0,001). Além disso, em todas essas variáveis, os voluntários estavam dentro da média de ± dois desvios-padrão, conforme verificado pelos gráficos de Bland-Altman. Esses resultados demonstram a validade do TT de 8 minutos, com dados mais robustos sendo observados pelo segundo estímulo deste protocolo. Palavras-chave: ciclismo, mountain bike, potência, FTP, teste de exercícios. RESUMEN La contrarreloj de 8 minutos (TT) es una alternativa metodológica a la TT de 60 minutos para evaluar la Potencia de Umbral Funcional (FTP) de los ciclistas, sin embargo, los estudios que probaron su validez no se encontraron en la literatura investigada. Por lo tanto, la investigación tiene como objetivo evaluar la validez del TT de 8 minutos. El estudio incluyó 9 ciclistas masculinos entrenados, con edades entre 25,46 ± 7,49 años, que fueron evaluados en tres días diferentes. El primer día, medimos datos personales, antropometría, umbrales ventilatorios y consumo máximo de oxígeno. Los otros días, enviamos a los voluntarios al TT de 8 y 60 minutos. Se analizó la concordancia entre los procedimientos utilizando el coeficiente de correlación intraclase (ICC) y su validez por Bland-Altman. Adoptamos un nivel de significancia del 5% y realizamos todos los análisis utilizando el SPSS. Los resultados sugieren un gran acuerdo, especialmente entre el segundo estímulo de 8 minutos y la prueba de referencia, para FTP (ICC: 0.792, p= 0.016), Wats por kilogramo (ICC: 0.952, p< 0.001), Wats por kilogramo de masa magra (ICC: 0.912, p= 0.001) y consumo máximo de oxígeno (ICC: 0.882, p= 0.001). Además, en todas estas variables, los voluntarios estuvieron dentro de la media ± dos desviaciones estándar, según lo verificado por las parcelas de Bland-Altman. Estos resultados demuestran la validez del TT de 8 minutos, con datos más robustos observados por el segundo estímulo de este protocolo. Palabras clave: ciclismo, bicicleta de montaña, potencia, FTP, prueba de ejercicio.
The 8-minute time trial (TT) is a methodological alternative to the 60-minute TT for evaluating the Functional Threshold Power (FTP) of cyclists, however, studies that tested its validity were not found in the researched literature. Therefore, research aims to assess the validity of the 8-minute TT. The study included 9 trained male cyclists, aged between 25.46 ± 7.49 years, who were assessed on three different days. On the first day, we measured personal data, anthropometrics, ventilatory thresholds and peak oxygen consumption. On the other days, we submitted the volunteers to the 8- and 60-minute TT. We analyzed the agreement between the procedures using the intraclass correlation coefficient (ICC) and its validity by Bland-Altman. We adopted a significance level of 5%, and we performed all analyses using the SPSS. The results suggest great agreement, especially between the second 8-minute stimulus and the reference test, for FTP (ICC: 0.792, p= 0.016), Wats per kilogram (ICC: 0.952, p< 0.001), Wats per kilogram of lean mass (ICC: 0.912, p= 0.001) and peak oxygen consumption (ICC: 0.882, p= 0.001). In addition, in all these variables, the volunteers were within the mean ± two standard deviations, as verified by the Bland-Altman plots. These results demonstrate the validity of the 8-minute TT, with more robust data being observed by the second stimulus of this protocol.
The estimated Functional Threshold Power (eFTP) obtained from a cyclists best 20-min effort, expressed in watts, and multiplied by 95% is sometimes overstated, especially for non-elite level cyclists or cyclists just starting out. Since Functional Threshold Power (FTP) is used for training prescription, in terms of creating power zones to train in, it is of utmost importance that the correct FTP is used. If FTP is overstated, a cyclist will overtrain at a level that is unrealistic to achieve. This research project aims at validating eFTP using a large, big data dataset, as well as to build out a supervised machine learning model, in the form of a weighted logistic regression, that can be used to predict FTP (pFTP), to create predictor of FTP that’s better than the currently accepted eFTP formula. Based on the data provided, our approach predicts FTP with far greater accuracy than eFTP, which turned out to overestimate cyclists’ FTP. The model outcome for each athlete, pFTP, was evaluated versus eFTP, by comparing each of these outcomes to the actual FTP (aFTP) observed in the data. To do this, Mean Squared Error and Mean Absolute Error were computed with pFTP producing values of 314.87 and 10.38 respectively when compared to aFTP, and eFTP producing 6417.32 and 61.16. With more accurate FTP estimations, cyclists are more likely to train in the correct power zones. It also validates that a laboratory environment is not always required when trying to validate eFTP and that a large, big data dataset can act as a valid substitute.
Scrutinizing functional threshold power via controversial concepts leads to confusion and discreditation of a valuable performance marker. This letter attempts to sort out the controversies.
Functional Threshold Power (FTP) has been considered a valid alternative to other performance markers that represent the upper boundary of the heavy intensity domain. However, such a claim has not been empirically examined from a physiological perspective.This study examined the blood lactate and VO2 response when exercising at and 15 W above the FTP (FTP+15W). Thirteen cyclists participated in the study. The VO2 was recorded continuously throughout FTP and FTP+15W, with blood lactate measured before the test, every 10 minutes and at task failure. Data were subsequently analysed using two-way ANOVA. The time to task failure at FTP and FTP+15W were 33.7 ± 7.6 and 22.0 ± 5.7 minutes (p < 0.001), respectively. The VO2peak was not attained when exercising at FTP+15W (VO2peak: 3.61 ± 0.81 vs FTP+15W 3.33 ± 0.68 L·min⁻¹, p < 0.001). The VO2 stabilised during both intensities. However, the end test blood lactate corresponding to FTP and FTP+15W was significantly different (6.7 ± 2.1 mM vs 9.2 ± 2.9 mM; p < 0.05). The VO2 response corresponding to FTP and FTP+15W suggests that FTP should not be considered a threshold marker between heavy and severe intensity.
The elegant concept of a hyperbolic relationship between power, velocity, or torque and time to exhaustion has rightfully captivated the imagination and inspired extensive research for over half a century. Theoretically, the relationship’s asymptote along the time axis (critical power, velocity, or torque) indicates the exercise intensity that could be maintained for extended durations, or the “heavy–severe exercise boundary”. Much more than a critical mass of the extensive accumulated evidence, however, has persistently shown the determined intensity of critical power and its variants as being too high to maintain for extended periods. The extensive scientific research devoted to the topic has almost exclusively centered around its relationships with various endurance parameters and performances, as well as the identification of procedural problems and how to mitigate them. The prevalent underlying premise has been that the observed discrepancies are mainly due to experimental ‘noise’ and procedural inconsistencies. Consequently, little or no effort has been directed at other perspectives such as trying to elucidate physiological reasons that possibly underly and account for those discrepancies. This review, therefore, will attempt to offer a new such perspective and point out the discrepancies’ likely root causes.
Abstract The resistance training volume along with the exercise range of motion has a significant impact on the training outcomes. Therefore, this study aimed to examine differences in training volume assessed by a number of performed repetitions, time under tension, and load–displacement as well as peak barbell velocity between the cambered and standard barbell bench press training session. The participants performed 3 sets to muscular failure of bench press exercise with the cambered or standard barbell at 50% of one-repetition maximum (1RM). Eighteen healthy men volunteered for the study (age = 25 ± 2 years; body mass = 92.1 ± 9.9 kg; experience in resistance training 7.3 ± 2.1 years; standard and cambered barbell bench press 1RM > 120% body mass). The t-test indicated a significantly higher mean range of motion for the cambered barbell in comparison to the standard (p
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.
Background
The peaking period for endurance competition is characterized for a relative increase of the intensity of training, after a longer period of training relatively dominated by lower intensity and higher volume
Objectives
The present study was designed to compare physiological and 10 km performance effects of high intensity training (HIT) versus race pace interval training (RP) during peaking for competition in well-trained runners.
Patients and Methods
13 athletes took part in the study, they were divided into two groups: HIT and RP. HIT performed short intervals at ~105% of the maximal aerobic velocity (MAV), while RP trained longer intervals at a speed of ~90% of the MAV (a speed approximating 10 km race pace). After 12 weeks of baseline training, the athletes trained for 6 weeks under one of the two peaking regimes. Subjects performed 10 km prior to and after the intervention period. The total load of training was matched between groups during the treatment phase. Subjects completed a graded treadmill running test until volitional exhaustion prior to each 10 km race. MAV was determined as the minimal velocity eliciting maximal oxygen consumption (VO2max).
Results
Both groups significantly improved their 10 km time (35 minutes 29 seconds ± 1 minutes 41 seconds vs 34 minutes 53 seconds ± 1 minutes 55 seconds, P < 0.01 for HIT; 35 minutes 27 seconds ± 1 minutes 40 seconds vs 34 minutes 53 seconds ± 1 minutes 18 seconds P < 0.01 for RP). VO2max increased after HIT (69 ± 3.6 vs 71.5 ± 4.2 ml.Kg⁻¹.min⁻¹, P < 0.05); while it didn’t for RP (68.4 ± 6 vs 69.8 ± 3 ml.Kg⁻¹.min⁻¹, p>0.05). In contrast, running economy decreased significantly after HIT (210 ± 6 ml.Kg⁻¹.km⁻¹ vs 218 ± 9, P < 0.05).
Conclusions
A 6 week period of training at either 105% of MAV or 90% of MAV yielded similar performance gains in a 10km race performed at ~90% MAV. Therefore, the physiological impact of HIT training seems to be positive for VO2max but negative for running economy.
This study aimed to analyze the agreement between five bar velocity monitoring devices, currently used in resistance training, to determine the most reliable device based on reproducibility (between-device agreement for a given trial) and repeatability (between-trial variation for each device). Seventeen resistance-trained men performed duplicate trials against seven increasing loads (20-30-40-50-60-70-80 kg) while obtaining mean, mean propulsive and peak velocity outcomes in the bench press, full squat and prone bench pull exercises. Measurements were simultaneously registered by two linear velocity transducers (LVT), two linear position transducers (LPT), two optoelectronic camera-based systems (OEC), two smartphone video-based systems (VBS) and one accelerometer (ACC). A comprehensive set of statistics for assessing reliability was used. Magnitude of errors was reported both in absolute (m s⁻¹) and relative terms (%1RM), and included the smallest detectable change (SDC) and maximum errors (MaxError). LVT was the most reliable and sensitive device (SDC 0.02–0.06 m s⁻¹, MaxError 3.4–7.1% 1RM) and the preferred reference to compare with other technologies. OEC and LPT were the second-best alternatives (SDC 0.06–0.11 m s⁻¹), always considering the particular margins of error for each exercise and velocity outcome. ACC and VBS are not recommended given their substantial errors and uncertainty of the measurements (SDC > 0.13 m s⁻¹).
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
This study aims to identify the measurement error associated with the mean movement velocity when using high-speed camera-based methods and video analysis during resistance training. Eleven resistance-trained men (26.0 ± 3.4 years) completed a progressive loading test in bench press exercise. Measurements from concentric mean velocity (MV), distance and time were obtained from a linear velocity transducer (T-Force) and videos recorded with high speed cameras on readily available smartphones (Samsung S6, Xiaomi A1, and iPhone X) and digital photo cameras (Casio FH20). Videos were examined using video analysis software (Kinovea). Despite the high correlations detected, the Bland-Altman analyses revealed that all high speed cameras produced substantial overestimation of barbell MV against high loads >60% 1RM (MV error = 0.06 ± 0.03 m·s-1 to 0.08 ± 0.04 m·s-1), but mainly against low loads <60% 1RM (MV error = 0.13 ± 0.06 m·s-1 to 0.20 ± 0.09 m·s-1). The maximum estimation error of the load being lifted (%1RM) was considerable both for low (8.5% to 12.7% 1RM) and high loads (13.9% to 22.6% 1RM). Among other practical limitations, the video-based system using different high-speed cameras and smartphone devices presents severe limitations when estimating mean concentric velocity, especially when recording low loads at high velocity.
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.
The main aim of this study is to assess the validity of a new cycling protocol to estimate the Maximal Lactate Steady-State workload (MLSS) through a one-day incremental protocol (1day_MLSS). Eleven well-trained male cyclists performed 3 to 4 trials of 30-min constant load test (48-72h in between) to determine their respective MLSS workload. Then, on separate days, each cyclist carried out two identical graded exercise tests, comprised of four 10-minute long stages, with the initial load at 63% of their respective maximal aerobic power, 0.2 W·Kg-1 increments, and blood lactate concentration [La] determinations each 5 min. The results of the 1day_MLSS tests were analysed through three different constructs: i) [La] difference between 5 th and 10 th min of each stage (DIF_5to10), ii) [La] difference between the 10 th min of two consecutive stages (DIF_10to10), and iii) difference in the mean [La] between the 5 th and 10 th min of two consecutive stages (DIF_mean). For all constructs, the physiological steady state was determined as the highest workload that could be maintained with a [La] rise lower than 1mmol·L-1. No significant differences were detected between the MLSS workload (247 ± 22W) and any of the 1day_MLSS data analysis (250 ± 24W, 245 ± 23W and 243 ± 21W, respectively; p>0.05). When compared to the MLSS workload, strong ICCs and low bias values were found for these three constructs, especially for the DIF_10to10 workload (r=0.960; Bias=2.2 W). High within-subject reliability data were found for the DIF10_10 construct (ICC=0.846; CV=0.4%; Bias=2.2 ± 6.4W). The 1day_MLSS test and DIF_10to10 data analysis is a valid assessment to predict the MLSS workload in cycling, that considerably reduces the dedicated time, effort and human resources that requires the original test. The validity and reliability values reported in this project are higher than those achieved by other previous MLSS estimation tests.
This study aimed to assess the relationship between an uphill time-trial (TT) performance and both aerobic and anaerobic parameters obtained from laboratory tests. Fifteen cyclists performed a Wingate anaerobic test, a graded exercise test (GXT) and a field-based 20-min TT with 2.7% mean gradient. After a 5-week non-supervised training period, 10 of them performed a second TT for analysis of pacing reproducibility. Stepwise multiple regressions demonstrated that 91% of TT mean power output variation (W kg-1) could be explained by peak oxygen uptake (ml kg-1.min-1) and the respiratory compensation point (W kg-1), with standardised beta coefficients of 0.64 and 0.39, respectively. The agreement between mean power output and power at respiratory compensation point showed a bias ± random error of 16.2 ± 51.8 W or 5.7 ± 19.7%. One-way repeated-measures analysis of variance revealed a significant effect of the time interval (123.1 ± 8.7; 97.8 ± 1.2 and 94.0 ± 7.2% of mean power output, for epochs 0-2, 2-18 and 18-20 min, respectively; P < 0.001), characterising a positive pacing profile. This study indicates that an uphill, 20-min TT-type performance is correlated to aerobic physiological GXT variables and that cyclists adopt reproducible pacing strategies when they are tested 5 weeks apart (coefficients of variation of 6.3; 1 and 4%, for 0-2, 2-18 and 18-20 min, respectively).
Purpose:
The mean power output (MPO) from a 60-min time trial (TT) - also known as "functional threshold power" or "FTP" - is a standard measure of cycling performance; however, shorter performance tests are desirable to reduce the burden of performance testing. We sought to determine the reliability of 4-min and 20-min TTs and the extent to which these short TTs were associated with 60-min MPO.
Methods:
Trained male cyclists (n = 8; age = 25 ± 5 years; VO2max = 71 ± 5 mL/kg/min) performed two 4-min TTs, two 20-min TTs, and one 60-min TT. Critical power (CP) was estimated from 4-min and 20-min TTs. The typical error of the mean (TEM) and intraclass correlation (ICC) were calculated to assess reliability, and R2 values were calculated to assess relationships with 60-min MPO.
Results:
Pairs of 4-min TTs (Mean: 417 [SD: 45] W vs. 412 [49] W, p. = 0.25; TEM = 8.1 W; ICC = 0.98), 20-min TTs (342 [36] W vs. 344 [33] W, p = 0.41; TEM = 4.6 W; ICC = 0.99), and CP estimates (323 [35] W vs. 328 [32] W, p = 0.25; TEM = 6.5; ICC = 0.98) were reliable. The 4-min MPO (R2 = 0.95), 20-min MPO (R2 = 0.92), estimated CP (R2 = 0.82), and combination of the 4-min and 20-min MPO (adj. R2 = 0.98) were strongly associated with the 60-min MPO (309 [26] W).
Conclusion:
The 4-min and 20-min TTs appear useful for assessing performance in trained, if not elite, cyclists.
Purpose: This study aimed to analyze the relationship between the Functional Threshold Power (FTP) and the Lactate Threshold (LT).
Methods: 20 male cyclists performed an incremental test in which the LT was determined. At least 48 h later, they performed a 20-minute time trial and 95% of the mean power output (P20) was defined as FTP. Participants were divided into recreational (Peak Power Output [PPO] < 4.5 W∙kg-1, n=11) or trained cyclists (PPO > 4.5 W∙kg-1, n=9) according to their fitness status.
Results: The FTP (240 ± 35 W) was overall not significantly different (effect size[ES]=0.20, limits of agreement [LoA]=-2.4 ± 11.5%) from the LT (246 ± 24 W), and both markers were strongly correlated (r=0.95, p<0.0001). Accounting for the participants’ fitness status, no significant differences were found between FTP and LT ([ES]=0.22; LoA=2.1 ± 7.8%) in TC, but FTP was significantly lower than the LT (p=0.0004, ES=0.81; LoA=-6.5 ± 8.3%) in RC. A significant relationship was found between relative PPO and the bias between FTP and the LT markers (r=0.77, p<0.0001).
Conclusions: The FTP is a valid field test-based marker for the assessment of endurance fitness. However, caution should be taken when using the FTP interchangeably with the LT as the bias between markers seems to depend on the athletes’ fitness status. Whereas the FTP provides a good estimate of the LT in trained cyclists, in recreational cyclists FTP may underestimate LT.
Purpose
The purpose of this study was to determine, i) the reliability of blood lactate and ventilatory-based thresholds, ii) the lactate threshold that corresponds with each ventilatory threshold (VT1 and VT2) and with maximal lactate steady state test (MLSS) as a proxy of cycling performance.
Methods
Fourteen aerobically-trained male cyclists (V˙O2max 62.1±4.6 ml·kg⁻¹·min⁻¹) performed two graded exercise tests (50 W warm-up followed by 25 W·min⁻¹) to exhaustion. Blood lactate, V˙O2 and V˙CO2 data were collected at every stage. Workloads at VT1 (rise in V˙E/V˙O2;) and VT2 (rise in V˙E/V˙CO2) were compared with workloads at lactate thresholds. Several continuous tests were needed to detect the MLSS workload. Agreement and differences among tests were assessed with ANOVA, ICC and Bland-Altman. Reliability of each test was evaluated using ICC, CV and Bland-Altman plots.
Results
Workloads at lactate threshold (LT) and LT+2.0 mMol·L⁻¹ matched the ones for VT1 and VT2, respectively (p = 0.147 and 0.539; r = 0.72 and 0.80; Bias = -13.6 and 2.8, respectively). Furthermore, workload at LT+0.5 mMol·L⁻¹ coincided with MLSS workload (p = 0.449; r = 0.78; Bias = -4.5). Lactate threshold tests had high reliability (CV = 3.4–3.7%; r = 0.85–0.89; Bias = -2.1–3.0) except for DMAX method (CV = 10.3%; r = 0.57; Bias = 15.4). Ventilatory thresholds show high reliability (CV = 1.6%–3.5%; r = 0.90–0.96; Bias = -1.8–2.9) except for RER = 1 and V-Slope (CV = 5.0–6.4%; r = 0.79; Bias = -5.6–12.4).
Conclusions
Lactate threshold tests can be a valid and reliable alternative to ventilatory thresholds to identify the workloads at the transition from aerobic to anaerobic metabolism.
The maximal lactate steady-state (MLSS) is frequently assessed for prescribing endurance exercise intensity. Knowledge of the intra-individual variability of the MLSS is important for practical application. To date, little is known about the reliability of time-to-exhaustion and physiological responses to exercise at MLSS. Twenty-one healthy men (age, 25.2 (SD 3.3) years; height, 1.83 (0.06) m; body mass, 78.9 (8.9) kg; maximal oxygen uptake, 57.1 (10.7) mL·min⁻¹·kg⁻¹) performed 1 incremental exercise test, and 2 constant-load tests to determine MLSS intensity. Subsequently, 2 open-end constant-load tests (MLSS 1 and 2) at MLSS intensity (3.0 (0.7) W·kg⁻¹, 76% (10%) maximal oxygen uptake) were carried out. During the tests, blood lactate concentrations, heart rate, ratings of perceived exertion (RPE), variables of gas exchange, and core body temperature were determined. Time-to-exhaustion was 50.8 (14.0) and 48.2 (16.7) min in MLSS 1 and 2 (mean change: −2.6 (95% confidence interval: −7.8, 2.6)), respectively. The coefficient of variation (CV) was high for time-to-exhaustion (24.6%) and for mean (4.8 (1.2) mmol·L⁻¹) and end (5.4 (1.7) mmol·L⁻¹) blood lactate concentrations (15.7% and 19.3%). The CV of mean exercise values for all other parameters ranged from 1.4% (core temperature) to 8.3% (ventilation). At termination, the CVs ranged from 0.8% (RPE) to 11.8% (breathing frequency). The low reliability of time-to-exhaustion and blood lactate concentration at MLSS indicates that the precise individual intensity prescription may be challenging. Moreover, the obtained data may serve as reference to allow for the separation of intervention effects from random variation in our sample.
The aim was to determine whether the midpoint between ventilatory thresholds (MPVT) corresponds to maximal lactate steady state (MLSS). Twelve amateur cyclists (21.0 ± 2.6 years old; 72.2 ± 9.0 kg; 179.8 ± 7.5 cm) performed an incremental test (25 W·min⁻¹) until exhaustion and several constant load tests of 30 minutes to determine MLSS, on different occasions. Using MLSS determination as the reference method, the agreement with five other parameters (MPVT; first and second ventilatory thresholds: VT1 and VT2; respiratory exchange ratio equal to 1: RER = 1.00; and Maximum) was analysed by the Bland-Altman method. The difference between workload at MLSS and VT1, VT2, RER=1.00 and Maximum was 31.1 ± 20.0, -86.0 ± 18.3, -63.6 ± 26.3 and -192.3 ± 48.6 W, respectively. MLSS was underestimated from VT1 and overestimated from VT2, RER = 1.00 and Maximum. The smallest difference (-27.5 ± 15.1 W) between workload at MLSS and MPVT was in better agreement than other analysed parameters of intensity in cycling. The main finding is that MPVT approached the workload at MLSS in amateur cyclists, and can be used to estimate maximal steady state.
Exercise intensity is arguably the most critical component of the exercise prescription model. It has been suggested that a threshold based model for establishing exercise intensity might better identify the lowest effective training stimulus for all individuals with varying fitness levels; however, experimental evidence is lacking. The purpose of this study was to compare the effectiveness of two exercise training programs for improving cardiorespiratory fitness: threshold based model vs. relative percent concept (i.e., % heart rate reserve - HRR).
Apparently healthy, but sedentary men and women (n = 42) were randomized to a non-exercise control group or one of two exercise training groups. Exercise training was performed 30 min/day on 5 days/week for 12weeks according to one of two exercise intensity regimens: 1) a relative percent method was used in which intensity was prescribed according to percentages of heart rate reserve (HRR group), or 2) a threshold based method (ACE-3ZM) was used in which intensity was prescribed according to the first ventilatory threshold (VT1) and second ventilatory threshold (VT2).
Thirty-six men and women completed the study. After 12weeks, VO2max increased significantly (p < 0.05 vs. controls) in both HRR (1.76 ± 1.93 mL/kg/min) and ACE-3ZM (3.93 ± 0.96 mL/kg/min) groups. Repeated measures ANOVA identified a significant interaction between exercise intensity method and change in VO2max values (F = 9.06, p < 0.05) indicating that VO2max responded differently to the method of exercise intensity prescription. In the HRR group 41.7 % (5/12) of individuals experienced a favorable change in relative VO2max (Δ > 5.9 %) and were categorized as responders. Alternatively, exercise training in the ACE-3ZM group elicited a positive improvement in relative VO2max (Δ > 5.9 %) in 100 % (12/12) of the individuals.
A threshold based exercise intensity prescription: 1). elicited significantly (p < 0.05) greater improvements in VO2max, and 2). attenuated the individual variation in VO2max training responses when compared to relative percent exercise training. These novel findings are encouraging and provide important preliminary data for the design of individualized exercise prescriptions that will enhance training efficacy and limit training unresponsiveness.
ClinicalTrials.gov Identifier: ID NCT02351713 Registered 30 January 2015.
A bioenergetical analysis of different exercise modes near maximal oxygen consumption (VO2max) intensity is scarce, hampering the prescription of training to enhance performance. We assessed the time sustained in swimming, rowing, running and cycling at an intensity eliciting VO2max and determined the specific oxygen uptake (VO2) kinetics and total energy expenditure (Etot-tlim).
Four sub-groups of 10 swimmers, 10 rowers, 10 runners and 10 cyclists performed: (i) an incremental protocol to assess the velocity (vVO2max) or power (wVO2max) associated with VO2max and (ii) a square wave transition exercise from rest to vVO2max/wVO2max to assess the time to voluntary exhaustion (Tlim-100%VO2max). The VO2 was measured using a telemetric portable gas analyser (K4b, Cosmed, Rome, Italy) and VO2 kinetics analysed using a double exponential curve fit. Etot-tlim was computed as the sum of its three components: aerobic (Aer), anaerobic lactic (Analac) and anaerobic alactic (Anaalac) contributions.
No differences were evident in Tlim-100%VO2max between exercise modes (swimming 187 ± 25, rowing 199 ± 52, running 245 ± 46 and cycling 227 ± 48 s; mean ± SD). In contrast, the VO2 kinetics profile exhibited a slower response in swimming (21 ± 3 s) compared with the other three modes of exercise (rowing 12 ± 3, running 10 ± 3 and cycling 16 ± 4 s) (P<0.001). Etot-tlim was similar between exercise modes even if the Analac contribution was smaller in swimming compared with the other sports (P<0.001).
Although there were different VO2 kinetics and ventilatory patterns, the Tlim-100%VO2max was similar between exercise modes most likely related to the common central and peripheral level of fitness in our athletes.
In Olympic combat sports, weight cutting is a common practice aimed to take advantage of competing in weight divisions below the athlete's normal weight. Fluid and food restriction in combination with dehydration (sauna and/or exercise induced profuse sweating) are common weight cut methods. However, the resultant hypohydration could adversely affect health and performance outcomes.
The aim of this study is to determine which of the routinely used non-invasive measures of dehydration best track urine osmolality, the gold standard non-invasive test.
Immediately prior to the official weigh-in of three National Championships, the hydration status of 345 athletes of Olympic combat sports (i.e., taekwondo, boxing and wrestling) was determined using five separate techniques: i) urine osmolality (UOSM), ii) urine specific gravity (USG), iii) urine color (UCOL), iv) bioelectrical impedance analysis (BIA), and v) thirst perception scale (TPS). All techniques were correlated with UOSM divided into three groups: euhydrated (G1; UOSM 250-700 mOsm·kg H2O-1), dehydrated (G2; UOSM 701-1080 mOsm·kg H2O-1), and severely dehydrated (G3; UOSM 1081-1500 mOsm·kg H2O-1).
We found a positive high correlation between the UOSM and USG (r = 0.89: p = 0.000), although this relationship lost strength as dehydration increased (G1 r = 0.92; G2 r = 0.73; and G3 r = 0.65; p = 0.000). UCOL showed a moderate although significant correlation when considering the whole sample (r = 0.743: p = 0.000) and G1 (r = 0.702: p = 0.000) but low correlation for the two dehydrated groups (r = 0.498-0.398). TPS and BIA showed very low correlation sizes for all groups assessed.
In a wide range of pre-competitive hydration status (UOSM 250-1500 mOsm·kg H2O-1), USG is highly associated with UOSM while being a more affordable and easy to use technique. UCOL is a suitable tool when USG is not available. However, BIA or TPS are not sensitive enough to detect hypohydration at official weight-in before an Olympic combat championship.
The purpose of this study was to investigate the effects of a 6-week aerobic training period on the time to fatigue (t
lim) during exercise performed at the maximal lactate steady state (MLSS). Thirteen untrained male subjects (TG; age 22.5 ± 2.4 years, body mass 72.9 ± 6.7 kg and VO2max 44.9 ± 4.8 mL kg−1 min−1) performed a cycle ergometer test until fatigue at the MLSS power output before and after 6 weeks of aerobic training. A group of eight control subjects (CG; age 25.1 ± 2.4 years, body mass 70.1 ± 9.8 kg and VO2max 45.2 ± 4.1 mL kg−1 min−1) also performed the two tests but did not train during the 6-week period. There were no differences between the groups with respect to the VO2max or MLSS power output (MLSSw) before the treatment period. The VO2max and the MLSSw of the TG increased by 11.2 ± 7.2 % (pre-treatment = 44.9 ± 4.8 vs. post-treatment = 49.8 ± 4.5 mL kg−1 min−1) and 14.7 ± 8.9 % (pre-treatment = 150 ± 27 vs. post-treatment = 171 ± 26 W), respectively, after 6 weeks of training. The results of the CG were unchanged. There were no differences in t
lim between the groups or within groups before and after training. Six weeks of aerobic training increases MLSSw and VO2max, but it does not alter the t
lim at the MLSS.
The maximal lactate steady state (MLSS) represents a submaximal intensity that may be important in prescribing both continuous and interval endurance training. This study compared time to exhaustion (TTE) at MLSS in continuous and intermittent (i.e., with pauses) exercise, investigating whether physiological variables differ between these exercise modes. Fourteen trained male cyclists volunteered for this investigation and performed an incremental test, several 30-min tests to determine two MLSS intensities (continuous and discontinuous protocol), and two randomized tests until exhaustion at MLSS intensities on a cycle ergometer. The intermittent or discontinuous protocol was performed using 5 min of cycling, with an interval of 1 min of passive rest. TTE at intermittent MLSS was 24% longer than TTE at continuous exercise (67.8 ± 14.3 min vs. 54.7 ± 10.9 min; p < 0.05; effect sizes = 1.04), even though the absolute power output of intermittent MLSS was higher than continuous (268 ± 29 W vs. 251 ± 29 W; p < 0.05). Additionally, the total mechanical work done was significantly lower at continuous exercise than at intermittent exercise. Likewise, regarding cardiorespiratory and metabolic variables, we observed greater responses during intermittent exercise than during continuous exercise at MLSS. Thus, for endurance training prescription, this is an important finding to apply in extensive interval sessions at MLSS. This result suggests that interval sessions at discontinuous MLSS should be used instead of continuous MLSS, as discontinuous MLSS allows for a larger amount of total work during the exhaustion trial.
The study aimed to assess the reproducibility of power output during a 4 min (TT4) and a 20 min (TT20) time-trial and the relationship with performance markers obtained during a laboratory graded exercise test (GXT). Ventilatory and lactate thresholds during a GXT were measured in competitive male cyclists (n=15; (.)VO (2max) 67+/-5 ml x min (-1) x kg (-1); P (max) 440+/-38W). Two 4 min and 20 min time-trials were performed on flat roads. Power output was measured using a mobile power-meter (SRM). Strong intraclass-correlations for TT4 ( R=0.98; 95% CL: 0.92-0.99) and TT20 ( R=0.98; 95% CL: 0.95-0.99) were observed. TT4 showed a bias+/-random error of - 0.8+/-23W or - 0.2+/-5.5%. During TT20 the bias+/-random error was - 1.8+/-14W or 0.6+/-4.4%. Both time-trials were strongly correlated with performance measures from the GXT (p<0.001). Significant differences were observed between power output during TT4 and GXT measures (p<0.001). No significant differences were found between TT20 and power output at the second lactate-turn-point (LTP2) (p=0.98) and respiratory compensation point (RCP) (p=0.97). In conclusion, TT4 and TT20 mean power outputs are reliable predictors of aerobic endurance. TT20 was in agreement with power output at RCP and LTP2.
We compared time to exhaustion (t
lim) at maximal lactate steady state (MLSS) between cycling and running, investigated if oxygen consumption, ventilation, blood lactate concentration, and perceived exertion differ between the exercise modes, and established whether MLSS can be determined for cycling and running using the same criteria. MLSS was determined in 15 moderately trained men (30 ± 6 years, 77 ± 6 kg) by several constant-load tests to exhaustion in cycling and running. Heart rate, oxygen consumption, and ventilation were recorded continuously. Blood lactate concentration and perceived exertion were measured every 5 min. t
lim (37.7 ± 8.9 vs. 34.4 ± 5.4 min) and perceived exertion (7.2 ± 1.7 vs. 7.2 ± 1.5) were similar for cycling and running. Heart rate (165 ± 8 vs. 175 ± 10 min−1; P < 0.01), oxygen consumption (3.1 ± 0.3 vs. 3.4 ± 0.3 l min−1; P < 0.001) and ventilation (93 ± 12 vs. 103 ± 16 l min−1; P < 0.01) were lower for cycling compared to running, respectively, whereas blood lactate concentration (5.6 ± 1.7 vs. 4.3 ± 1.3 mmol l−1; P < 0.05) was higher for cycling. t
lim at MLSS is similar for cycling and running, despite absolute differences in heart rate, ventilation, blood lactate concentration, and oxygen consumption. This may be explained by the relatively equal cardiorespiratory demand at MLSS. Additionally, the similar t
lim for cycling and running allows the same criteria to be used for determining MLSS in both exercise modes.
A recent study has shown the reproducibility of time to exhaustion (time limit: tlim) at the lowest velocity that elicits the maximal oxygen consumption (vVO2 max). The same study found an inverse relationship between this time to exhaustion at vVO2 max and vVO2 max among 38 élite long-distance runners (Billat et al. 1994b). The purpose of the present study was to compare the time to exhaustion at the power output (or velocity) at VO2 max for different values of VO2 max, depending on the type of exercise and not only on the aerobic capacity. The time of exhaustion at vVO2 max (tlim) has been measured among 41 élite (national level) sportsmen: 9 cyclists, 9 kayak paddlers, 9 swimmers and 14 runners using specific ergometers. Velocity or power at VO2 max (vVO2 max) was determined by continuous incremental testing. This protocol had steps of 2 min and increments of 50 W, 30 W, 0.05 m s-1 and 2 km-1 for cyclists, kayak paddlers, swimmers and runners, respectively. One week later, tlim was determined under the same conditions. After a warm-up of 10 min at 60% of their vVO2 max, subjects were concluded (in less than 45 s) to their vVO2 max and then had to sustain it as long as possible until exhaustion. Mean values of vVO2 max and tlim were respectively equal to 419 +/- 49 W (tlim = 222 +/- 91 s), 239 +/- 56 W (tlim = 376 +/- 134 s), 1.46 +/- 0.09 m s-1 (tlim = 287 +/- 160 s) and 22.4 +/- 0.8 km h-1 (tlim = 321 +/- 84 s), for cyclists, kayak paddlers, swimmers and runners. Time to exhaustion at vVO2 max was only significantly different between cycling and kayaking (ANOVA test, p < 0.05). Otherwise, VO2 max (expressed in ml min-1 kg-1) was significantly different between all sports except between cycling and running (p < 0.05). In this study, time to exhaustion at vVO2 max was also inversely related to VO2 max for the entire group of élite sportsmen (r = -0.320, p < 0.05, n = 41). The inverse relationship between VO2 max and tlim at vVO2 max has to be explained, it seems that tlim depends on VO2 max regardless of the type of exercise undertaken.
Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
While warm up is considered to be essential for optimum performance, there is little scientific evidence supporting its effectiveness in many situations. As a result, warm-up procedures are usually based on the trial and error experience of the athlete or coach, rather than on scientific study. Summarising the findings of the many warm-up studies conducted over the years is difficult. Many of the earlier studies were poorly controlled, contained few study participants and often omitted statistical analyses. Furthermore, over the years, warm up protocols consisting of different types (e.g. active, passive, specific) and structures (e.g. varied intensity, duration and recovery) have been used. Finally, while many studies have investigated the physiological responses to warm up, relatively few studies have reported changes in performance following warm up. The first part of this review critically analyses reported changes in performance following various active warm-up protocols. While there is a scarcity of well-controlled studies with large subject numbers and appropriate statistical analyses, a number of conclusions can be drawn regarding the effects of active warm up on performance. Active warm up tends to result in slightly larger improvements in short-term performance (<10 seconds) than those achieved by passive heating alone. However, short-term performance may be impaired if the warm-up protocol is too intense or does not allow sufficient recovery, and results in a decreased availability of high-energy phosphates before commencing the task. Active warm up appears to improve both long-term (>/=5 minutes) and intermediate performance (>10 seconds, but <5 minutes) if it allows the athlete to begin the subsequent task in a relatively non-fatigued state, but with an elevated baseline oxygen consumption (VO(2)). While active warm up has been reported to improve endurance performance, it may have a detrimental effect on endurance performance if it causes a significant increase in thermoregulatory strain. The addition of a brief, task-specific burst of activity has been reported to provide further ergogenic benefits for some tasks. By manipulating intensity, duration and recovery, many different warm-up protocols may be able to achieve similar physiological and performance changes. Finally, passive warm-up techniques may be important to supplement or maintain temperature increases produced by an active warm up, especially if there is an unavoidable delay between the warm up and the task and/or the weather is cold. Further research is required to investigate the role of warm up in different environmental conditions, especially for endurance events where a critical core temperature may limit performance.
We hypothesized that a period of endurance training would result in a speeding of muscle phosphocreatine concentration ([PCr]) kinetics over the fundamental phase of the response and a reduction in the amplitude of the [PCr] slow component during high-intensity exercise. Six male subjects (age 26 +/- 5 yr) completed 5 wk of single-legged knee-extension exercise training with the alternate leg serving as a control. Before and after the intervention period, the subjects completed incremental and high-intensity step exercise tests of 6-min duration with both legs separately inside the bore of a whole-body magnetic resonance spectrometer. The time-to-exhaustion during incremental exercise was not changed in the control leg [preintervention group (PRE): 19.4 +/- 2.3 min vs. postintervention group (POST): 19.4 +/- 1.9 min] but was significantly increased in the trained leg (PRE: 19.6 +/- 1.6 min vs. POST: 22.0 +/- 2.2 min; P < 0.05). During step exercise, there were no significant changes in the control leg, but end-exercise pH and [PCr] were higher after vs. before training. The time constant for the [PCr] kinetics over the fundamental exponential region of the response was not significantly altered in either the control leg (PRE: 40 +/- 13 s vs. POST: 43 +/- 10 s) or the trained leg (PRE: 38 +/- 8 s vs. POST: 40 +/- 12 s). However, the amplitude of the [PCr] slow component was significantly reduced in the trained leg (PRE: 15 +/- 7 vs. POST: 7 +/- 7% change in [PCr]; P < 0.05) with there being no change in the control leg (PRE: 13 +/- 8 vs. POST: 12 +/- 10% change in [PCr]). The attenuation of the [PCr] slow component might be mechanistically linked with enhanced exercise tolerance following endurance training.
Jeffries, O, Simmons, R, Patterson, SD, and Waldron, M. Functional threshold power is not equivalent to lactate parameters in trained cyclists. J Strength Cond Res XX(X): 000-000, 2019-Functional threshold power (FTP) is derived from a maximal self-paced 20-minute cycling time trial whereby the average power output is scaled by 95%. However, the physiological basis of the FTP concept is unclear. Therefore, we evaluated the relationship of FTP with a range of laboratory-based blood lactate parameters derived from a submaximal threshold test. Twenty competitive male cyclists completed a maximal 20-minute time trial and an incremental exercise test to establish a range of blood lactate parameters. Functional threshold power (266 ± 42 W) was strongly correlated (r = 0.88, p < 0.001) with the power output associated with a fixed blood lactate concentration 4.0 mmol·L (LT4.0) (268 ± 30 W) and not significantly different (p > 0.05). While mean bias was 2.9 ± 24.6 W, there were large limits of agreement (LOA) between FTP and LT4.0 (-45 to 51 W). All other lactate parameters, lactate threshold (LT) (236 ± 32 W), individual anaerobic threshold (244 ± 33 W), and LT thresholds determined using the Dmax method (221 ± 25 W) and modified Dmax method (238 ± 32 W) were significantly different from FTP (p < 0.05). While FTP strongly correlated with LT4.0, the large LOA refutes any equivalence as a measure with physiological basis. Therefore, we would encourage athletes and coaches to use alternative field-based methods to predict cycling performance.
Exercising at the maximal lactate steady state (MLSS) results in increased but stable metabolic responses. We tested the hypothesis that even a slight increase above MLSS (10 W), by altering the metabolic steady‐state, would reduce exercise performance capacity. Eleven trained men in our study performed: one ramp‐incremental tests; two to four 30‐min constant‐load cycling exercise trials to determine the PO at MLSS (MLSSp), and ten watts above MLSS (MLSSp+10), which were immediately followed by a time‐to‐exhaustion test; and a time‐to‐exhaustion test with no‐prior exercise. Pulmonary O2 uptake (V̇O2) and blood lactate concentration ([La‐]b) as well as local muscle O2 extraction ([HHb]) and muscle activity (EMG) of the vastus lateralis (VL) and rectus femoris (RF) muscles were measured during the testing sessions. When exercising at MLSSp+10, although V̇O2 was stable, there was an increase in ventilatory responses and EMG activity, along with a non‐stable [La‐]b response (P<0.05). The [HHb] of VL muscle achieved its apex at MLSSp with no additional increase above this intensity, whereas the [HHb] of RF progressively increased during MLSSp+10 and achieved its apex during the time‐to‐exhaustion trials. Time‐to‐exhaustion performance was decreased after exercising at MLSSp (37.3±16.4%) compared to the no‐prior exercise condition, and further decreased after exercising at MLSSp+10 (64.6±6.3%) (P<0.05). In summary, exercising for 30 min slightly above MLSS led to significant alterations of metabolic responses which disproportionately compromised subsequent exercise performance. Furthermore, the [HHb] signal of VL seemed to achieve a “ceiling” at the intensity of exercise associated with MLSS.
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Functional threshold power is defined as the highest power output a cyclist can maintain in a quasi-steady state for approximately 60 min (FTP60). In order to improve practicality for regular evaluations, FTP60 could theoretically be determined as 95% of the mean power output in a 20-min time trial (FTP20). This study tested this assumption and the validity of FTP20 and FTP60 against the individual anaerobic threshold (IAT). Twenty-three trained male cyclists performed an incremental test to exhaustion, 20- and 60-min time trials, and a time to exhaustion at FTP20. Power output, heart rate and oxygen uptake representing FTP20, FTP60 and IAT were not different (p>0.05), and large to very large correlations were found (r=0.61 to 0.88). Bland-Altman plots between FTP20, FTP60 and IAT showed small bias (-1 to -5 W), but large limits of agreement ([-40 to 32 W] to [-62 to 60 W]). Time to exhaustion at FTP20 was 50.9±15.7 min. In conclusion, FTP20 and FTP60 should not be used interchangeably on an individual basis and their validity against IAT should be interpreted with caution.
Purpose:
To validate the new drive indoor trainer Hammer designed by Cycleops®.
Methods:
Eleven cyclists performed 44 randomized and counterbalanced graded exercise tests (100-500W), at 70, 85 and 100 rev.min-1 cadences, in seated and standing positions, on 3 different Hammer units, while a scientific SRM system continuously recorded cadence and power output data.
Results:
No significant differences were detected between the three Hammer devices and the SRM for any workload, cadence, or pedalling condition (P value between 1.00 and 0.350), except for some minor differences (P 0.03 and 0.04) found in the Hammer 1 at low workloads, and for Hammer 2 and 3 at high workloads, all in seated position. Strong ICCs were found between the power output values recorded by the Hammers and the SRM (≥0.996; P=0.001), independently from the cadence condition and seated position. Bland-Altman analysis revealed low Bias (-5.5-3.8) and low SD of Bias (2.5-5.3) for all testing conditions, except marginal values found for the Hammer 1 at high cadences and seated position (9.6±6.6). High absolute reliability values were detected for the 3 Hammers (150-500W; CV<1.2%; SEM<2.1).
Conclusions:
This new Cycleops trainer is a valid and reliable device to drive and measure power output in cyclists, providing an alternative to larger and more expensive laboratory ergometers, and allowing cyclists to use their own bicycle.
A link between lactate and muscular exercise was seen already more than 200 years ago. The blood lactate concentration (BLC) is sensitive to changes in exercise intensity and duration. Multiple BLC threshold concepts define different points on the BLC power curve during various tests with increasing power (INCP). The INCP test results are affected by the increase in power over time. The maximal lactate steady state (MLSS) is measured during a series of prolonged constant power (CP) tests. It detects the highest aerobic power without metabolic energy from continuing net lactate production, which is usually sustainable for 30 to 60 min. BLC threshold and MLSS power are highly correlated with the maximum aerobic power and athletic endurance performance. The idea that training at threshold intensity is particularly effective has no evidence. Three BLC-orientated intensity domains have been established: (1) training up to an intensity at which the BLC clearly exceeds resting BLC, light- and moderate-intensity training focusing on active regeneration or high-volume endurance training (Intensity < Threshold); (2) heavy endurance training at work rates up to MLSS intensity (Threshold ≤ Intensity ≤ MLSS); and (3) severe exercise intensity training between MLSS and maximum oxygen uptake intensity mostly organized as interval and tempo work (Intensity > MLSS). High-performance endurance athletes combining very high training volume with high aerobic power dedicate 70 to 90% of their training to intensity domain 1 (Intensity < Threshold) in order to keep glycogen homeostasis within sustainable limits.
This study was undertaken to compare training-induced changes in selected physiological, body composition and performance variables following two training periodization models: traditional (TP) versus block periodization (BP). Ten world-class kayakers were assessed four times during a training cycle over two consecutive seasons. On each occasion, subjects completed an incremental test to exhaustion on the kayak ergometer to determine peak oxygen uptake (VO(2peak)), VO(2) at second ventilatory threshold (VO(2) VT2), peak blood lactate, paddling speed at VO(2peak) (PS(peak)) and VT2 (PS( VT2)), power output at VO(2peak) (Pw(peak)) and VT2 (Pw( VT2)), stroke rate at VO(2peak) (SR(peak)) and VT2 (SR( VT2)) as well as heart rate at VO(2peak) and VT2. Volume and exercise intensity were quantified for each endurance training session. Both TP and BP cycles resulted in similar gains in VO(2peak) (11 and 8.1%) and VO(2) VT2 (9.8 and 9.4%), even though the TP cycle was 10 weeks and 120 training hours longer than the BP cycle. Following BP paddlers experienced larger gains in PS(peak), Pw(peak) and SR(peak) than those observed with TP. These findings suggest that BP may be more effective than TP for improving the performance of highly trained top-level kayakers. Although both models allowed significant improvements of selected physiological and kayaking performance variables, the BP program achieved similar results with half the endurance training volume used in the TP model. A BP design could be a more useful strategy than TP to maintain the residual training effects as well as to achieve greater improvements in certain variables related to kayaking performance.
This study was undertaken to analyze changes in selected cardiovascular and neuromuscular variables in a group of elite kayakers across a 12-week periodized cycle of combined strength and endurance training. Eleven world-class level paddlers underwent a battery of tests and were assessed four times during the training cycle (T0, T1, T2, and T3). On each occasion subjects completed an incremental test to exhaustion on the kayak-ergometer to determine maximal oxygen uptake (VO2max), second ventilatory threshold (VT2), peak blood lactate, paddling speed at VO2max (PSmax) and at VT2 (PSVT2), stroke rate at VO2max and at VT2, heart rate at VO2max and at VT2. One-repetition maximum (1RM) and mean velocity with 45% 1RM load (V
45%) were assessed in the bench press (BP) and prone bench pull (PBP) exercises. Anthropometric measurements (skinfold thicknesses and muscle girths) were also obtained. Training volume and exercise intensity were quantified for each of three training phases (P1, P2, and P3). Significant improvements in VO2max (9.5%), VO2 at VT2 (9.4%), PSmax (6.2%), PSVT2 (4.4%), 1RM in BP (4.2%) and PBP (5.3%), V
45% in BP (14.4%) and PBP (10.0%) were observed from T0 to T3. A 12-week periodized strength and endurance program with special emphasis on prioritizing the sequential development of specific physical fitness components in each training phase (i.e. muscle hypertrophy and VT2 in P1, and maximal strength and aerobic power in P2) seems effective for improving both cardiovascular and neuromuscular markers of highly trained top-level athletes.
The aim of the present study was to analyze the net joint moment distribution, joint forces and kinematics during cycling to exhaustion. Right pedal forces and lower limb kinematics of ten cyclists were measured throughout a fatigue cycling test at 100% of PO(MAX). The absolute net joint moments, resultant force and kinematics were calculated for the hip, knee and ankle joint through inverse dynamics. The contribution of each joint to the total net joint moments was computed. Decreased pedaling cadence was observed followed by a decreased ankle moment contribution to the total joint moments in the end of the test. The total absolute joint moment, and the hip and knee moments has also increased with fatigue. Resultant force was increased, while kinematics has changed in the end of the test for hip, knee and ankle joints. Reduced ankle contribution to the total absolute joint moment combined with higher ankle force and changes in kinematics has indicated a different mechanical function for this joint. Kinetics and kinematics changes observed at hip and knee joint was expected due to their function as power sources. Kinematics changes would be explained as an attempt to overcome decreased contractile properties of muscles during fatigue.
Laboratory and field assessments were made on eighteen male distance runners. Performance data were obtained for distances of 3.2, 9.7, 15, 19.3 km (n = 18) and the marathon (n = 13). Muscle fiber composition expressed as percent of slow twitch fibers (%ST), maximal oxygen consumption (VO2max), running economy (VO2 for a treadmill velocity of 268 m/min), and the VO2 and treadmill velocity corresponding to the onset of plasma lactate accumulation (OPLA) were determined for each subject. %ST (R > or equal to .47), VO2max (r > or equal to .83), running economy (r > or equal to .49), VO2 in ml/kg min corresponding to the OPLA (r > or equal to .91) and the treadmill velocity corresponding to OPLA (r > or equal to .91) were significantly (p < .05) related to performance at all distances. Multiple regression analysis showed that the treadmill velocity corresponding to the OPLA was most closely related to performance and the addition of other factors did not significantly raise the multiple R values suggesting that these other variables may interact with the purpose of keeping plasma lactates low during distance races. The slowest and fastest marathoners ran their marathons 7 and 3 m/min faster than their treadmill velocities corresponding to their OPLA which indicates that this relationship is independent of the competitive level of the runner. Runners appear to set a race pace which allows the utilization of the largest possible VO2 which just avoids the exponential rise in plasma lactate.
Maximal lactate steady state (MLSS) refers to the upper limit of blood lactate concentration indicating an equilibrium between lactate production and lactate elimination during constant workload. The aim of the present study was to investigate whether different levels of MLSS may explain different blood lactate concentration (BLC) levels at submaximal workload in the sports events of rowing, cycling, and speed skating. Eleven rowers (mean +/- SD, age 20.1 +/- 1.5 yr, height 188.7 +/- 6.2 cm, weight 82.7 +/- 8.0 kg), 16 cyclists and triathletes (age 23.6 +/- 3.0 yr, height 181.4 +/- 5.6 cm, weight 72.5 +/- 6.2 kg), and 6 speed skaters (age 23.3 +/- 6.6 yr, height 179.5 +/- 7.5 cm, weight 73.2 +/- 5.6 kg) performed an incremental load test to determine maximal workload and several submaximal 30-min constant workloads for MLSS measurement on a rowing ergometer, a cycle ergometer, and on a speed-skating track. Maximal workload was higher (P < or = 0.05) in rowing (416.8 +/- 46.2 W) than in cling (358.6 +/- 34.4 W) and speed skating (383.5 +/- 40.9 W). The level of MLSS differed (P < or = 0.001) in rowing (3.1 +/- 0.5 mmol.l-1), cycling (5.4 +/- 1.0 mmol.l-1), and in speed skating (6.6 +/- 0.9 mmol.l-1). MLSS workload was higher (P < or = 0.05) in rowing (316.2 +/- 29.9 W) and speed skating (300.5 +/- 43.8 W) than in cycling (257.8 +/- 34.6 W). No differences (P > 0.05) in MLSS workload were found between speed skating and rowing. MLSS workload intensity as related to maximal workload was independent (P > 0.05) of the sports event: 76.2% +/- 5.7% in rowing, 71.8% +/- 4.1% in cycling, and 78.1% +/- 4.4% in speed skating. Changes in MLSS do not respond with MLSS workload, the MLSS workload intensity, or with the metabolic profile of the sports event. The observed differences in MLSS and MLSS workload may correspond to the sport-specific mass of working muscle.
To evaluate the physiological capacities and performance of professional road cyclists in relation to their morphotype-dependent speciality.
24 world-class cyclists, classified as flat terrain (FT, N = 5), time trial (TT, N = 4), all terrain (AT, N = 6). and uphill (UH, N = 9) specialists, completed an incremental laboratory cycling test to assess maximal power output (Wmax), maximal oxygen uptake (VO2max), lactate threshold (LT), and onset of blood lactate accumulation (OBLA).
UH had a higher frontal area (FA):body mass (BM) ratio (5.23 +/- 0.09 m2 x kg(-1) x 10(-3)) than FT and TT (P < 0.05). FT showed the highest absolute Wmax (481 +/- 18 W), and UH the highest Wmax relative to BM (6.47 +/- 0.33 W x kg(-1)). WLT and W(OBLA) values were significantly higher in FT (356 +/- 41 and 417 +/- 45 W) and TT (357 +/- 41 and 409 +/- 46 W) than in UH (308 +/- 46 and 356 +/- 41). Scaling of these values relative to FA and BM exponents 0.32 and 0.79 minimized group differences, but considerable differences among mean group values remained. FT and TT had the highest Wmax per FA unit (1300 +/- 62 and 1293 +/- 57 W x m2), whereas TT had the highest absolute W x kg(-0.32) and W x kg(-0.79), as well as W x kg(-0.32), W x kg(-0.79), and W x m2 at the LT and OBLA.
i) Scaling of maximal and submaximal physiological values showed a performance advantage of TT over FT, AT, and UH in all cycling terrains and conditions; and ii) mass exponents of 0.32 and 1 were the most appropriate to evaluate level and uphill cycling ability, respectively, whereas absolute Wmax values are recommended for performance-prediction in short events on level terrain, and W(LT) and W(OBLA) in longer time trials and uphill cycling.
The aim of this study is to show the relationship between test-retest reproducibility and responsiveness and to introduce the smallest real difference (SRD) approach, using the sickness impact profile (SIP) in chronic stroke patients as an example. Forty chronic stroke patients were interviewed twice by the same examiner, with a 1-week interval. All patients were interviewed during the qualification period preceding a randomized clinical trial. Test-retest reproducibility has been quantified by the intraclass correlation coefficient (ICC). the standard error of measurement (SEM) and the related smallest real difference (SRD). Responsiveness was defined as the ratio of the clinically relevant change to the SD of the within-stable-subject test-retest differences. The ICC for the total SIP was 0.92, whereas the ICCs for the specified SIP categories varied from 0.63 for the category 'recreation and pastime' to 0.88 for the category 'work'. However, both the SEM and the SRD far more capture the essence of the reproducibility of a measurement instrument. For instance, a total SIP score of an individual patient of 28.3% (which is taken as an example, being the mean score in the study population) should decrease by at least 9.26% or approximately 13 items, before any improvement beyond reproducibility noise can be detected. The responsiveness to change of a health status measurement instrument is closely related to its test-retest reproducibility. This relationship becomes more evident when the SEM and the SRD are used to quantify reproducibility, than when ICC or other correlation coefficients are used.
It is assumed that the maximal lactate steady state (MLSS) can be used to establish the highest workload that can be maintained over time without continual blood lactate accumulation. In untrained subjects, and in both elite and junior athletes, MLSS occurs at different blood lactate concentrations (BLC) for different exercise modes. This suggests that MLSS depends on the motor pattern of exercise and may be a function of the relationship between power output per unit muscle mass and the mass of the muscle primarily engaged in the activity. A computer model has been developed that takes account of current theories relating to the effect of exercise on BLC and to the factors that limit oxygen transport to the muscle cell. Simulations using this model support the suggestion that load per unit of engaged muscle mass accounts for task-specific levels of MLSS. Simulated differences in MLSS appear because the MLSS does not necessarily reflect the real maximal equilibrium between lactate formation and utilization, the LLSS. The higher difference between MLSS and LLSS measured in rowing ergometry compared to cycle ergometry seems to indicate a greater task sensitivity of the BLC response to given changes of exercise intensity during rowing. Whether such a difference may be relevant for a deeper understanding of task-specific training strategies remains a matter for further investigation.
The maximal lactate steady state (MLSS) is defined as the highest blood lactate concentration that can be maintained over time without a continual blood lactate accumulation. The objective of the present study was to analyze the effects of pedal cadence (50 vs. 100 rev min(-1)) on MLSS and the exercise workload at MLSS (MLSS(workload)) during cycling. Nine recreationally active males (20.9+/-2.9 years, 73.9+/-6.5 kg, 1.79+/-0.09 m) performed an incremental maximal load test (50 and 100 rev min(-1)) to determine anaerobic threshold (AT) and peak workload (PW), and between two and four constant submaximal load tests (50 and 100 rev min(-1)) on a mechanically braked cycle ergometer to determine MLSS(workload) and MLSS. MLSS(workload) was defined as the highest workload at which blood lactate concentration did not increase by more than 1 mM between minutes 10 and 30 of the constant workload. The maximal lactate steady state intensity (MLSS(intensity)) was defined as the ratio between MLSS(workload) and PW. MLSS(workload) (186.1+/-21.2 W vs. 148.2+/-15.5 W) and MLSS(intensity) (70.5+/-5.7% vs. 61.4+/-5.1%) were significantly higher during cycling at 50 rev min(-1) than at 100 rev min(-1), respectively. However, there was no significant difference in MLSS between 50 rev min(-1) (4.8+/-1.6 mM) and 100 rev min(-1) (4.7+/-0.8 mM). We conclude that MLSS(workload) and MLSS(intensity) are dependent on pedal cadence (50 vs. 100 rev min(-1)) in recreationally active individuals. However, this study showed that MLSS is not influenced by the different pedal cadences analyzed.
In the present study we investigated whether a high volume of cycling training would influence the metabolic changes associated with a succession of three exhaustive cycling exercises.
Seven professional road cyclists (VO2max: 74.3 +/- 3.7 mL.min.kg; maximal power tolerated: 475 +/- 18 W; training: 22 +/- 3 h.wk) and seven sport sciences students (VO2max: 54.2 +/- 5.3 mL.min.kg; maximal power tolerated: 341 +/- 26 W; training: 6 +/- 2 h.wk) performed three different exhaustive cycling exercise bouts (progressive, constant load, and sprint) on an electrically braked cycloergometer positioned near the magnetic resonance scanner. Less than 45 s after the completion of each exercise bout, recovery kinetics of high-energy phosphorylated compounds and pH were measured using P-MR spectroscopy.
Resting values for phosphomonoesters (PME) and phosphodiesters (PDE) were significantly elevated in the cyclist group (PME/ATP: 0.82 +/- 0.11 vs 0.58 +/- 0.19; PDE/ATP: 0.27 +/- 0.03 vs 0.21 +/- 0.05). Phosphocreatine (PCr) consumption and inorganic phosphate (Pi) accumulation measured at end of exercise bouts 1 (PCr: 6.5 +/- 3.2 vs 10.4 +/- 1.6 mM; Pi: 1.6 +/- 0.7 vs 6.8 +/- 3.4 mM) and 3 (PCr: 5.6 +/- 2.4 vs 9.3 +/- 3.9 mM; Pi: 1.5 +/- 0.5 vs 7.7 +/- 3.3 mM) were reduced in cyclists compared with controls. During the recovery period after each exercise bout, the pH-recovery rate was larger in professional road cyclists, whereas the PCr-recovery kinetics were significantly faster for cyclists only for bout 3.
Whereas the PDE and PME elevation at rest in professional cyclists may indicate fiber-type changes and an imbalance between glycogenolytic and glycolytic activity, the lower PCr consumption during exercise and the faster pH-recovery kinetic clearly suggest an improved mitochondrial function.