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The Importance of ‘Durability’ in the Physiological Profiling of Endurance Athletes

DOI:10.1007/s40279-021-01459-0
Authors:
Ed Maunder at Auckland University of Technology
Ed Maunder
Stephen Seiler at Universitetet i Agder
Stephen Seiler
Mathew Mildenhall at High Performance Sport New Zealand
Mathew Mildenhall
Andrew Kilding at Auckland University of Technology
Andrew Kilding
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Abstract and Figures

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.
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Sports Medicine (2021) 51:1619–1628
https://doi.org/10.1007/s40279-021-01459-0
REVIEW ARTICLE
The Importance of‘Durability’ inthePhysiological Profiling
ofEndurance Athletes
EdMaunder1 · StephenSeiler2· MathewJ.Mildenhall3· AndrewE.Kilding1,3· DanielJ.Plews1
Accepted: 29 March 2021 / Published online: 22 April 2021
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
Abstract
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.
Key Points
Applied exercise physiologists working with endurance
athletes routinely profile a number of physiological traits
for purposes of training programming and monitoring.
The common models for these assessments do not
account for changes in profiled variables over time
during long-duration exercise, and, therefore, athlete
‘durability’, which we define and discuss here.
Using existing data and field measures from a range of
endurance athletes, we propose that applied exercise
physiologists would benefit from development of models
that incorporate interactions between exercise intensity
and duration, and therefore quantify athlete ‘durability’.
* Ed Maunder
ed.maunder@aut.ac.nz
1 Sports Performance Research Institute New Zealand,
Auckland University ofTechnology, Auckland, NewZealand
2 Faculty ofHealth andSport Sciences, University ofAgder,
Kristiansand, Norway
3 High Performance Sport New Zealand, Auckland,
NewZealand
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Power output at the boundaries between the moderate, heavy, and severe intensity domains are routinely used to assess performance capability, regulate training load and competition intensities, and to quantify adaptations to training (Burnley and Jones 2018;Jones et al. 2019;Maunder et al. 2021). However, we and others have observed that the power outputs observed at these intensity transitions decreases over time during prolonged exercise (Clark et al. 2018a(Clark et al. , 2019aStevenson et al. 2022). ...
... However, we and others have observed that the power outputs observed at these intensity transitions decreases over time during prolonged exercise (Clark et al. 2018a(Clark et al. , 2019aStevenson et al. 2022). This has implications for the application of physiological profiling data collected in well-rested athletes to prolonged training sessions (Maunder et al. 2021). ...
... Identification of a physiological marker that changes over time during prolonged exercise in accordance with changes in the intensity domain transitions would be useful for withinsession intensity regulation, and could result in a more precise calculation of training intensity distribution (Maunder et al. 2021). We previously observed that the classic upward drift in heart rate during prolonged cycling was proportionally greater than the downward drift in power output at the Communicated by Susan Hopkins. ...
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Purpose: To quantify the effects of prolonged cycling on the rate of ventilation ([Formula: see text]), frequency of respiration (FR), and tidal volume (VT) associated with the moderate-to-heavy intensity transition. Methods: Fourteen endurance-trained cyclists and triathletes (one female) completed an assessment of the moderate-to-heavy intensity transition, determined as the first ventilatory threshold (VT1), before (PRE) and after (POST) two hours of moderate-intensity cycling. The power output, [Formula: see text], FR, and VT associated with VT1 were determined PRE and POST. Results: As previously reported, power output at VT1 significantly decreased by ~ 10% from PRE to POST. The [Formula: see text] associated with VT1 was unchanged from PRE to POST (72 ± 12 vs. 69 ± 13 L.min-1, ∆ - 3 ± 5 L.min-1, ∆ - 4 ± 8%, P = 0.075), and relatively consistent (within-subject coefficient of variation, 5.4% [3.7, 8.0%]). The [Formula: see text] associated with VT1 was produced with increased FR (27.6 ± 5.8 vs. 31.9 ± 6.5 breaths.min-1, ∆ 4.3 ± 3.1 breaths.min-1, ∆ 16 ± 11%, P = 0.0002) and decreased VT (2.62 ± 0.43 vs. 2.19 ± 0.36 L.breath-1, ∆ - 0.44 ± 0.22 L.breath-1, ∆ - 16 ± 7%, P = 0.0002) in POST. Conclusion: These data suggest prolonged exercise shifts ventilatory parameters at the moderate-to-heavy intensity transition, but [Formula: see text] remains stable. Real-time monitoring of [Formula: see text] may be a useful means of assessing proximity to the moderate-to-heavy intensity transition during prolonged exercise and is worthy of further research.
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... For example, a relatively low DFAa-1 value during a standardised warm-up (indicating autonomic fatigue) prior to a planned interval session may be used to modify the training intensity of the session . Further, the change in the DFA-a1 to the VO 2 /power/speed ratio during exercise could help assess "durability", previously defined as "the time of onset and magnitude of deterioration in physiological-profiling characteristics over time during prolonged exercise" (Maunder et al., 2021). In support of these concepts, a previous study showed that the DFA-a1 value was significantly lower after a 6-h simulated ultramarathon when running at a speed close to VT1 when compared to the pre-ultramarathon value . ...
... However, the magnitude of DFA-a1 reduction at VT1 in the second ramp showed only trivial correlations with VO 2peak or weekly training hours. The observation that DFA-a1 behaviour during fatigue is not directly associated with conventional fitness markers such as VO 2peak , could suggest that this outcome provides additional information that may be helpful to predict athletic performance or recovery ability (Maunder et al., 2021). However, it is important to note that the ramp protocol may have led to differences in the magnitude of fatigue between individuals for example due differences in the time to exhaustion once VO 2peak was achieved, which may also have impacted these correlations. ...
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... There are certain influencing factors there that can affect parameters and provoke certain drifts. In the current literature, thermoregulatory capabilities, substrate metabolism, or muscle fiber type profile are mentioned as examples for such factors [34]. The authors currently refer to the concept of 'durability', defined as the 'time of onset and magnitude of deterioration in physiological-profiling characteristics during prolonged exercise' [34]. ...
... In the current literature, thermoregulatory capabilities, substrate metabolism, or muscle fiber type profile are mentioned as examples for such factors [34]. The authors currently refer to the concept of 'durability', defined as the 'time of onset and magnitude of deterioration in physiological-profiling characteristics during prolonged exercise' [34]. Even though 'prolonged exercise' usually refers to exercise durations in the range of hours, certain effects are evident even within the presented 40 min. ...
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With Norway's successes in middle and long-distance running, lactate-guided threshold training has regained importance in recent years. Therefore, the aim of the present study was to investigate the individual responses on common monitoring parameters based on a lactate-guided conventional training method. In total, 15 trained runners (10 males, 5 females; 18.6 ± 3.3 years; VO2max : 59.3 ± 5.9 mL kg −1 min −1) completed a 40-min continuous running session at a fixed lactate threshold load of 2 mmol L−1. Lactate (La), oxygen uptake (VO2), heart rate (HR), and rating of perceived exertion (RPE) were recorded. The chosen workload led to lactate values of 2.85 ± 0.56 mmol L −1 (range: 1.90-3.80), a percentage of VO 2max utilization (%VO2max) of 79.2 ± 2.5% (range: 74.9-83.8), a percentage of HR max utilization (%HRmax) of 92.2 ± 2.5% (range: 88.1-95.3), and an RPE of 6.1 ± 1.9 (range: 3-10) at the end of the running session. Thereby, the individual responses differed considerably. These results indicate that a conventional continuous training method based on a fixed lactate threshold can lead to different individual responses, potentially resulting in various physiological impacts. Moreover, correlation analyses suggest that athletes with higher lactate threshold performance levels must choose their intensity in continuous training methods more conservatively (lower percentage intensity based on a fixed threshold) to avoid eliciting excessively strong metabolic responses.
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... The addition of short 30-s sprint intervals to long rides has been demonstrated to be well-tolerated by elite cyclists and may enhance fatigue-resistance/durability [79]. Importantly, durability, or the time of onset and magnitude of deterioration in physiological performance characteristics during prolonged exercise, may be a critical predictor of performance in endurance sport [80][81][82]. Almquist et al. [79] demonstrated that the addition of maximal sprint intervals during a 2-week high-volume cycling training camp allowed for the maintenance of gross economy in a semi-fatigued state compared to reductions in gross economy in the non-sprint group, suggesting durability was improved with the sprint training [79]. However, as the addition of maximal-intensity intervals with the maintenance of typical training load is a technique used to drive underperformance (overreaching) [83], this type of training should be considered in the context of the desired training-intensity distribution and overall training load, and not simply prescribed in addition to regular training. ...
A Perspective on High-Intensity Interval Training for Performance and Health
Article
Full-text available
Interval training is a simple concept that refers to repeated bouts of relatively hard work interspersed with recovery periods of easier work or rest. The method has been used by high-level athletes for over a century to improve performance in endurance-type sports and events such as middle- and long-distance running. The concept of interval training to improve health, including in a rehabilitative context or when practiced by individuals who are relatively inactive or deconditioned, has also been advanced for decades. An important issue that affects the interpretation and application of interval training is the lack of standardized terminology. This particularly relates to the classification of intensity. There is no common definition of the term “high-intensity interval training” (HIIT) despite its widespread use. We contend that in a performance context, HIIT can be characterized as intermittent exercise bouts performed above the heavy-intensity domain. This categorization of HIIT is primarily encompassed by the severe-intensity domain. It is demarcated by indicators that principally include the critical power or critical speed, or other indices, including the second lactate threshold, maximal lactate steady state, or lactate turnpoint. In a health context, we contend that HIIT can be characterized as intermittent exercise bouts performed above moderate intensity. This categorization of HIIT is primarily encompassed by the classification of vigorous intensity. It is demarcated by various indicators related to perceived exertion, oxygen uptake, or heart rate as defined in authoritative public health and exercise prescription guidelines. A particularly intense variant of HIIT commonly termed “sprint interval training” can be distinguished as repeated bouts performed with near-maximal to “all out” effort. This characterization coincides with the highest intensity classification identified in training zone models or exercise prescription guidelines, including the extreme-intensity domain, anaerobic speed reserve, or near-maximal to maximal intensity classification. HIIT is considered an essential training component for the enhancement of athletic performance, but the optimal intensity distribution and specific HIIT prescription for endurance athletes is unclear. HIIT is also a viable method to improve cardiorespiratory fitness and other health-related indices in people who are insufficiently active, including those with cardiometabolic diseases. Research is needed to clarify responses to different HIIT strategies using robust study designs that employ best practices. We offer a perspective on the topic of HIIT for performance and health, including a conceptual framework that builds on the work of others and outlines how the method can be defined and operationalized within each context.
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... Moreover, although participants of both groups were followed up for a similar duration and had a comparable age at the end of the follow-up, it cannot be discarded that some participants included in the Non-Pro group could later transition (ie, after the end of the present study) to the professional category. Another limitation is the fact that some physiological indicators such as anaerobic capacity (as assessed with the Wingate test), gross muscle efficiency (computed during submaximal, constant-load exercise), or durability (ie, the ability to attenuate the deterioration in physiological or performance indicators under fatigue conditions 33 ) were not assessed. The lack of data for other important variables such as training experience before the study, actual training loads, or dietary habits is also a limitation. ...
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Purpose: Laboratory-based indicators are commonly used for performance assessment in young cyclists. However, evidence supporting the use of these indicators mostly comes from cross-sectional research, and their validity as predictors of potential future performance remains unclear. We aimed to assess the role of laboratory variables for predicting transition from U23 (under 23 y) to professional category in young cyclists. Methods: Sixty-five U23 male road cyclists (19.6 [1.5] y) were studied. Endurance (maximal graded test and simulated 8-min time trial [TT]), muscle strength/power (squat, lunge, and hip thrust), and body composition (assessed with dual-energy X-ray absorptiometry) indicators were determined. Participants were subsequently followed and categorized attending to whether they had transitioned ("Pro") or not ("Non-Pro") to the professional category during the study period. Results: The median follow-up period was 3 years. Pro cyclists (n = 16) showed significantly higher values than Non-Pro riders (n = 49) for ventilatory thresholds, peak power output, peak oxygen uptake, and TT performance (all P < .05, effect size > 0.69) and lower levels of fat mass and bone mineral content/density (P < .05, effect size > 0.63). However, no significant differences were found for muscle strength/power indicators (P > .05, effect size < 49). The most accurate individual predictor was TT performance (overall predictive value = 76% for a cutoff value of 5.6 W·kg-1). However, some variables that did not reach statistical significance in univariate analyses contributed significantly to a multivariate model (R2 = .79, overall predictive value = 94%). Conclusions: Although different "classic" laboratory-based endurance indicators can predict the potential of reaching the professional category in U23 cyclists, a practical indicator such as 8-minute TT performance showed the highest prediction accuracy.
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... Heart rate (HR) as regulated by the autonomic nervous system may be used as a proxy of whole-body fatigue during physical exercise training sessions and thereby overcomes several of the limitations of other methods. However, the validity of HR as a proxy of fatigue has been questioned, for example due to effect of HR (cardiac) drift or the indication of opposing trends in adaptation processes (Mattsson et al., 2011;Maunder et al., 2021;Schimpchen et al., 2023). The variability of cardiac beat-to-beat intervals (i.e., heart rate variability; HRV) may be more useful to inform on the degree of autonomous fatigue during endurance training or competition. ...
The Between-Day Reliability of Correlation Properties of Heart Rate Variability During Running
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... Another term that has been used to describe the concept is high-intensity repeatability which highlights that durability is about repeating high-intensity maximum efforts. 5 The term durability is difficult to translate into Dutch. 'Duurzaamheid' does not seem to fully cover the meaning and also brings up irrelevant associations with, among other things, the energy transition. ...
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Performance-determining variables are usually measured from a rested state and not after prolonged exercise, specific to when athletes compete for the win in long-distance events. Purpose: (1) To compare cross-country skiing double-poling (DP) performance and the associated physiological and biomechanical performance-determining variables between a rested state and after prolonged exercise and (2) to investigate whether the relationship between the main performance-determining variables and DP performance is different after prolonged submaximal DP than when tested from a rested state. Methods: Male cross-country skiers (N = 26) performed a blood lactate profile test and an incremental test to exhaustion from a rested state on day 1 (D1; all using DP) and after 90-minute submaximal DP on day 2 (D2). Results: The DP performance decreased following prolonged submaximal DP (D1: peak speed = 15.33-20.75 km·h-1, median = 18.1 km·h-1; D2: peak speed = 13.68-19.77 km·h-1, median = 17.8 km·h-1; z = -3.96, P < .001, effect size r = -.77), which coincided with a reduced submaximal gross efficiency and submaximal and peak cycle length, with no significant change in peak oxygen uptake (P = .26, r = .23). The correlation coefficient between D1 cycle length at 12 km·h-1 and D2 performance is significantly smaller than the correlation coefficient between D2 cycle length at 12 km·h-1 and D2 performance (P = .033), with the same result being found for peak cycle length (P < .001). Conclusions: The reduced DP performance after prolonged submaximal DP coincided with a reduced submaximal gross efficiency and shorter peak cycle length. The results indicate that performance-determining variables could be determined after prolonged exercise to gain more valid insight into long-distance DP performance.
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Response to considerations regarding Maximal Lactate Steady State determination before redefining the gold‐standard
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Purpose: To quantify the repeated oxygen deficits attained during intermittent enduranceexercise by measuring oxygen consumption O2) and oxygen demand O2 dem) throughout a simulated roller ski race. Methods: Eight male elite cross-country skiers O2,peak 77.4 ± 4.4 mL∙min-1∙kg-1) raced a 13.5 km roller ski time-trial on a World Cup course. On two additional days, athletes completed (i) 6 sub-maximal loads (à 5 min) and a ~ 4 min maximal trial to establish athlete-specific estimates of skiing economy, O2,peak, and maximal ΣO2 def (MAOD); and (ii) a simulation of the time-trial on a roller skiing treadmill. During the simulation, external work rate (Pprop) and skiing speed (v) were adjusted to match the Pprop and v measured during the time-trial, and pulmonary O2 was measured breath-by-breath. O2 dem and ΣO2 def were calculated using an athlete-specific model for skiing economy throughout the treadmill simulation. Results: During the treadmill simulation O2 was on average 0.77 O2,peak, and active O2 dem, (i.e., excluding the time in simulated downhills), was on average 1.01 O2,peak. The athletes repeatedly attained substantial oxygen deficits in individual uphill sections of the treadmill simulation, but the deficits were typically small compared to their MAOD (average 14%, range ~0-50%). However, the ΣO2 def summed over all periods of active propulsion was on average 3.8MAOD. Conclusion: Athletes repeatedly attain substantial oxygen deficits in the uphill segments of a distance cross-country ski race. Furthermore, the total accumulated oxygen deficit of all these segments is several times higher than the athletes’ MAODs. This suggests that rapid recovery of the energy stores represented by the oxygen deficit is necessary during downhill sections, and that this might be an important determinant of distance skiing performance.
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Resting skeletal muscle ATGL and CPT1b are associated with peak fat oxidation rates in men and women but do not explain observed sex‐differences
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New findings: What is the central question of this study? To explore the relationship between proteins in skeletal muscle and adipose tissue determined at rest and peak rates of fat oxidation in men and women. What is the main finding and its importance? Resting content of proteins in skeletal muscle involved in triglyceride hydrolysis and mitochondrial lipid transport are more strongly associated with peak fat oxidation rates than proteins related to lipid transport, or hydrolysis in adipose tissue. Whilst females display higher relative rates of fat oxidation than males, this was unexplained by the proteins measured in this study, suggesting other factors determine sex-differences in fat metabolism. Abstract: This study explored key proteins involved in fat metabolism that may associate with peak fat oxidation (PFO) and account for sexual dimorphism in exercise fuel metabolism. Thirty-six healthy adults [15 females; age 40 (11); V̇O2 peak 42.5 (9.5) mL⋅kg BM-1 ⋅min-1 ; means±SD] completed two exercise tests to determine PFO via indirect calorimetry. Resting adipose tissue and/or skeletal muscle biopsies were obtained to determine the protein content of adipose tissue PLIN1, CGI-58, HSL, ATGL, ACSL1, CPT1b and oestrogen receptor α (ERα), and skeletal muscle FABPpm, ATGL, ACSL1, CTP1b and ERα. Moderate strength correlations were found between PFO (mg⋅kg FFM-1 ⋅min-1 ) and the protein content of ATGL [rs = 0.41 (0.03-0.68), P<0.05] and CPT1b [rs = 0.45 (0.09-0.71), P<0.05] in skeletal muscle. No other statistically significant bivariate correlations were consistently found. Females had a greater relative PFO compared to males: 7.1±1.9 vs 4.5±1.3 and 7.3±1.7 vs 4.8±1.2 mg⋅kg FFM-1 ⋅min-1 )] in the adipose tissue (n = 14) and skeletal muscle (n = 12) sub-groups, respectively (p<0.05). No statistically significant sex differences were found in the content of these proteins. The regulation of PFO may involve processes relating to intramyocellular triglyceride hydrolysis and mitochondrial fatty acid transport, and adipose tissue is likely to play a more minor role than muscle. Sex differences in fat metabolism are likely to be due to factors other than the resting content of proteins in skeletal muscle and adipose tissue relating to triglyceride hydrolysis and fatty acid transport. This article is protected by copyright. All rights reserved.
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Is the Functional Threshold Power a Valid Metric to Estimate the Maximal Lactate Steady State in Cyclists?
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  • Nov 2019
Lillo-Beviá, JR, Courel-Ibáñez, J, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, and Pallarés, JG. Is the functional threshold power a valid metric to estimate the maximal lactate steady state in cyclists? J Strength Cond Res XX(X): 000-000, 2019-The aims of this study were to determine (a) the repeatability of a 20-minute time-trial (TT20), (b) the location of the TT20 in relation to the main physiological events of the aerobic-anaerobic transition, and (c) the predictive power of a list of correction factors and linear/multiple regression analysis applied to the TT20 result to estimate the individual maximal lactate steady state (MLSS). Under laboratory conditions, 11 trained male cyclists and triathletes (V[Combining Dot Above]O2max 59.7 ± 3.0 ml·kg·min) completed a maximal graded exercise test to record the power output associated with the first and second ventilatory thresholds and V[Combining Dot Above]O2max measured by indirect calorimetry, several 30 minutes constant tests to determine the MLSS, and 2 TT20 tests with a short warm-up. Very high repeatability of TT20 tests was confirmed (standard error of measurement of ±3 W and smallest detectable change of ±9 W). Validity results revealed that MLSS differed substantially from TT20 (bias = 26 ± 7 W). The maximal lactate steady state was then estimated from the traditional 95% factor (bias = 12 ± 7 W) and a novel individual correction factor (ICF% = MLSS/TT20), resulting in 91% (bias = 1 ± 6 W). Complementary linear (MLSS = 0.7488 × TT20 + 43.24; bias = 0 ± 5 W) and multiple regression analysis (bias = 0 ± 4 W) substantially improved the individual MLSS workload estimation. These findings suggest reconsidering the TT20 procedures and calculations to increase the effectiveness of the MLSS prediction.
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Maximal Lactate Steady State Versus the 20-Minute Functional Threshold Power Test in Well-Trained Individuals: “Watts” the Big Deal?
Article
  • Nov 2019
Purpose: To (1) compare the power output (PO) for both the 20-minute functional threshold power (FTP20) field test and the calculated 95% (FTP95%) with PO at maximal lactate steady state (MLSS) and (2) evaluate the sensitivity of FTP95% and MLSS to training-induced changes. Methods: Eighteen participants (12 males: 37 [6] y and 6 females: 28 [6] y) performed a ramp-incremental cycling test to exhaustion, 2 to 3 constant-load MLSS trials, and an FTP20 test. A total of 10 participants returned to repeat the test series after 7 months of training. Results: The PO at FTP20 and FTP95% was greater than that at MLSS (P = .00), with the PO at MLSS representing 88.5% (4.8%) and 93.1% (5.1%) of FTP and FTP95%, respectively. MLSS was greater at POST compared with PRE training (12 [8] W) (P = .002). No increase was observed in mean PO at FTP20 and FTP95% (P = .75). Conclusions: The results indicate that the PO at FTP95% is different to MLSS, and that changes in the PO at MLSS after training were not reflected by FTP95%. Even when using an adjusted percentage (ie, 88% rather than 95% of FTP20), the large variability in the data is such that it would not be advisable to use this as a representation of MLSS.
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