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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.
... 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.
... 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.
... 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.
... 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.ResultsLIT 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 behaviour 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.
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. 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. 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 (R2 = 0.99, SEE = 8.9 W, Percentage SEE (%SEE) = 5.1%, P < 0.001 and R2 = 0.99, SEE = 10.0 W, %SEE = 5.7%, P < 0.001, respectively). 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.
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 (WAnT mean ), (ii) maximal oxygen consumption, (iii) respiratory compensation threshold (VT 2 ), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of 5%–9%. Power outputs were 710 ± 63 W for WAnT mean , 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT 2 and 247 ± 20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P < 0.001). The CP predictions overestimated TTE at WAnT mean (00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT 2 (–04:18 ± 03:20 mm:ss; P < 0.05), and correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or lower power outputs (e.g., sprints and 40-km time trials).
Novelty CP calculation enables to predict TTE at any cycling power output. We tested those predictions against measured TTE in a wide range of cycling power outputs. CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.
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