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

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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
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
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
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,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... It must be noted, nonetheless, that most methods that are available for the profiling of endurance athletes do not account for the influence of fatigue, which is likely to rise with the duration of the effort. 4 This is also the case for the RPP, which could affect the validity of this method as an indicator of actual endurance performance. ...
... Our findings suggest that MMP values decline markedly even after relatively low levels of fatigue (average decline of −1.6% to −3.0% after 15 kJ·kg −1 ), which supports the notion that fatigue can affect physiological profiling characteristics that unfortunately are usually measured under nonfatigue conditions. 4 Thus, if MMP values assessed under rest conditions were used for training intensity prescription, this could potentially result in excessively high workloads, with cyclists likely unable to attain the target intensity under fatigue conditions. These findings would be in line with the welldocumented shift observed during prolonged exercise for other physiological variables, notably the internal to external workload ratio. ...
... For instance, maintaining a constant speed or wattage (ie, constant external workload) during long-lasting running (eg, a marathon) or cycling (4-h cycling session) is associated with gradually increasing levels of heart rate or self-perceived exertion (ie, increasing internal workload). 4 As recently highlighted by Maunder et al, 4 there is a need for considering another marker of endurance capacity, "durability"-that is, the time of onset (and magnitude) of deterioration in physiological profiling characteristics during prolonged exercise-for ensuring an adequate prescription and monitoring of training loads, as well as for programming pacing strategies during prolonged exercise. Thus, it might be advisable to assess physiological or performance indicators not only under rest conditions but also with different levels of fatigue. ...
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Purpose: The present study aimed to determine the influence of fatigue on the record power profile of professional male cyclists. We also assessed whether fatigue could differently affect cyclists of 2 competition categories. Methods: We analyzed the record power profile in 112 professional cyclists (n = 46 and n = 66 in the ProTeam [PT] and WorldTour [WT] category, respectively; age 29 [6] y, 8 [5] y experience in the professional category) during 2013-2021 (8 [5] seasons/cyclist). We analyzed their mean maximal power (MMP) values for efforts lasting 10 seconds to 120 minutes with no fatigue (after 0 kJ·kg-1) and with increasing levels of fatigue (after 15, 25, 35, and 45 kJ·kg-1). Results: A significant (P < .001) and progressive deterioration of all MMP values was observed from the lowest levels of fatigue assessed (ie, -1.6% to -3.0% decline after 15 kJ·kg-1, and -6.0% to -9.7% after 45 kJ·kg-1). Compared with WT, PT cyclists showed a greater decay of MMP values under fatigue conditions (P < .001), and these differences increased with accumulating levels of fatigue (decay of -1.8 to -2.9% [WT] with reference to 0 kJ·kg-1 vs -1.1% to -4.4% [PT] after 15 kJ·kg-1 and of -4.7% to -8.8% [WT] vs -7.6% to -11.6% [PT] after 45 kJ·kg-1). No consistent differences were found between WT and PT cyclists in MMP values assessed in nonfatigue conditions (after 0 kJ·kg-1), but WT cyclists attained significantly higher MMP values with accumulating levels of fatigue, particularly for long-duration efforts (≥5 min). Conclusions: Our findings highlight the importance of considering fatigue when assessing the record power profile of endurance athletes and support the ability to attenuate fatigue-induced decline in MMP values as a determinant of endurance performance.
... It is plausible that better athletes may be able to preserve physiological traits, and thus maintain speeds closer to CS. Indeed, it has recently been suggested that durability, defined as deterioration in physiological characteristics over time during prolonged exercise [19], should be taken into consideration during physiological and performance profiling. ...
... Athletes experiencing a decoupling < 1.1 in the last segment of the race were classified as low decoupling, a decoupling ≥ 1.1 but < 1.2 was considered as moderate, and if the decoupling was ≥ 1.2 it was deemed as high decoupling [19]. In order to investigate whether decoupling experienced by an athlete contributed to explain marathon performance, the correlation between key decoupling characteristics (i.e., magnitude and the onset of decoupling) and absolute (marathon time) and relative (marathon speed relative to CS) marathon performance was determined. ...
... However, there was considerable inter-individual variability in the magnitude of decoupling. Athletes were classified, based on the magnitude of the decoupling observed in the last 5 km segment of the marathon, as low, moderate and high decoupling, as previously suggested [19]. Despite this being an arbitrary classification, we found a remarkably even distribution, and each of the three decoupling groups contained ~ 33% of athletes in the sample. ...
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Aim This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance. Methods The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Internal workload was determined as a percentage of maximum heart rate, and external workload as speed relative to estimated critical speed (CS). Decoupling magnitude (i.e., decoupling in the 35–40 km segment relative to the 5–10 km segment) was classified as low (< 1.1), moderate (≥ 1.1 but < 1.2) or high (≥ 1.2). Decoupling onset was calculated when decoupling exceeded 1.025. Results The overall internal-to-external workload decoupling experienced was 1.16 ± 0.22, first detected 25.2 ± 9.9 km into marathon running. The low decoupling group (34.5% of runners) completed the marathon at a faster relative speed (88 ± 6% CS), had better marathon performance (217.3 ± 33.1 min), and first experienced decoupling later in the marathon (33.4 ± 9.0 km) compared to those in the moderate (32.7% of runners, 86 ± 6% CS, 224.9 ± 31.7 min, and 22.6 ± 7.7 km), and high decoupling groups (32.8% runners, 82 ± 7% CS, 238.5 ± 30.7 min, and 19.1 ± 6.8 km; all p < 0.01). Compared to females, males’ decoupling magnitude was greater (1.17 ± 0.22 vs. 1.12 ± 0.16; p < 0.01) and occurred earlier (25.0 ± 9.8 vs. 26.3 ± 10.6 km; p < 0.01). Marathon performance was associated with the magnitude and onset of decoupling, and when included in marathon performance models utilising CS and the curvature constant, prediction error was reduced from 6.45 to 5.16%. Conclusion Durability characteristics, assessed as internal-to-external workload ratio, show considerable inter-individual variability, and both its magnitude and onset are associated with marathon performance.
... Though recognized measures of endurance exercise induced fatigue have been explored, none are of practical value while in the midst of an exercise session or race. There are also questions regarding the validity of heart rate drift as a sign of fatigue during exercise (Maunder et al., 2021). HR drift appears to be a complex phenomenon (Souissi et al., 2021) that can normalize or even reverse with very long endurance efforts (Mattsson et al., 2011). ...
... Extrapolation of the observation above could also lead to usage of DFA a1 as a marker of both "exercise durability" and as a method for daily decisions about "training readiness". In a recent publication, athlete "durability" was described as "the time of onset and magnitude of deterioration in physiological-profiling characteristics over time during prolonged exercise" (Maunder et al., 2021). In other words, durability is an assessment of fatigue related reduction in performance, as opposed to standard measures of athletic fitness such as VO 2MAX or maximal lactate steady state. ...
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While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.
... In addition, a "fourth variable, " neuromuscular power/anaerobic capacity, plays an important role in the decisive end phase of tactical track races [12]. Further, classic laboratory testing may not capture a "fifth variable, " fatigue resistance associated with specific adaptations that delay muscular deterioration and fatigue and enable maintaining race pace over the final 7-10 km of an elite marathon [13,14]. Different time courses in the development of these performance determinants are very likely. ...
... The preponderance of scientific and results-proven practice recommends that intensity scales/zones/domains in LDR should be based on physiological parameters (e.g., heart rate ranges, ventilatory/ lactate thresholds), external work rates (running pace or types of training), or perceived exertion [17, 18, 21, 22, 25, 27, 28, 30, 40-42, 54, 112, 135, 163-165], but no consensus has so far been established. We would argue that this lack of consensus is consistent with an uncomfortable truth; no single intensity parameter performs satisfactorily in isolation as an intensity guide due to (1) intensity-duration interactions and uncoupling of internal and external workload, (2) individual and day-to-day variation, and (3) strain responses that can carry over from preceding workouts and transiently disrupt these relationships [13,166,167]. Consequently, combining external load, internal load, and perception regularly during training provides a triangulation of intensity characteristics that is probably complimentary and informative. Whatever intensity parameter that is chosen, describing and comparing training characteristics requires a common intensity scale. ...
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In this review we integrate the scientific literature and results-proven practice and outline a novel framework for understanding the training and development of elite long-distance performance. Herein, we describe how fundamental training characteristics and well-known training principles are applied. World-leading track runners (i.e., 5000 and 10,000 m) and marathon specialists participate in 9 ± 3 and 6 ± 2 (mean ± SD) annual competitions, respectively. The weekly running distance in the mid-preparation period is in the range 160–220 km for marathoners and 130–190 km for track runners. These differences are mainly explained by more running kilometers on each session for marathon runners. Both groups perform 11–14 sessions per week, and ≥ 80% of the total running volume is performed at low intensity throughout the training year. The training intensity distribution vary across mesocycles and differ between marathon and track runners, but common for both groups is that volume of race-pace running increases as the main competition approaches. The tapering process starts 7–10 days prior to the main competition. While the African runners live and train at high altitude (2000–2500 m above sea level) most of the year, most lowland athletes apply relatively long altitude camps during the preparation period. Overall, this review offers unique insights into the training characteristics of world-class distance runners by integrating scientific literature and results-proven practice, providing a point of departure for future studies related to the training and development in the Olympic long-distance events.
... However, repeated maximal performance (of similar durations) is a determinant of success in several individual and team sports formats, which may require athletes to contest a heat and a final, or multiple rounds of competition in one day e.g., athletics, cycling or rugby sevens. Therefore, a proposed extension to the line of inquiry established by the present work is that of either or both athlete durability (as per Maunder et al. [70]) and repeatability, where athletes' decay in performance is assessed during prolonged exercise or with and without sufficient recovery between trials, respectively. ...
... Intertest reliability is an important determinant of assessing change within an athlete as a result of a nutritional or training intervention, with pacing and performance levels being pertinent factors that are documented to influence three-minute all-out test performance [40,69,70]. Average power and end power obtained during a three-minute all-out test have been shown to be reliable between repeated trials [48,49], hence our focus on these metrics as performance thresholds for indicating meaningful change between interventions, as per Figure 2. We are satisfied with this interpretation, as an intervention is deemed meaningful if it exceeds the typical variation of the test, when compared to another condition. ...
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Previous menthol studies have demonstrated ergogenic effects in endurance-based activity. However, there is a need for research in sports whose physiological requirements exceed maximal aerobic capacity. This study assessed the effects of 0.1% menthol mouth-rinsing upon a modified three-minute maximal test in the heat (33.0 ± 3.0 °C; RH 46.0 ± 5.0%). In a randomised crossover single blind placebo-controlled study, 11 participants completed three modified maximal tests, where each trial included a different mouth rinse: either menthol (MEN), cold water (WAT) or placebo (PLA). Participants were asked to rate their thermal comfort (TC), thermal sensation (TS) and rating of perceived exertion (RPE) throughout the test. Heart rate, core temperature, oxygen uptake (VȮ2), ventilation (VĖ) and respiratory exchange ratio (RER) were monitored continuously throughout the test, alongside cycling power variables (W; W/kg). A blood lactate (BLa) level was taken pre-and post-test. Small to moderate effects (Cohen's d and accompanying 90% confidence intervals) between solutions MEN, WAT and PLA were observed towards the end of the test in relation to relative power. Specifically, from 75-105 s between solutions MEN and WAT (ES: 0.795; 90% CI: 0.204 to 1.352) and MEN and PLA (ES: 1.059; 90% CI: 0.412 to 1.666), this continued between MEN and WAT (ES: 0.729; 90% CI: 0.152 to 1.276) and MEN and PLA (ES: 0.791; 90% CI: 0.202 to 1.348) from 105-135 s. Between 135-165 s there was a moderate difference between solutions MEN and WAT (ES: 1.058; 90% CI: 0.411 to 1.665). This indicates participants produced higher relative power for longer durations with the addition of the menthol mouth rinse, compared to cold water or placebo. The use of menthol (0.1%) as a mouth rinse showed small performance benefits for short duration high intensity exercise in the heat.
... These findings align with previous work highlighting the importance of fatigue resistance (van Erp, Sanders, et al. 2021;Maunder et al. 2021). The predictive ability for VAMmean and Speedmean is questionable as VAMmean could only be significantly predicted in Cat 2, while Speedmean was only predicted in Cat 1 climbs. ...
... In previous work, we found that U23 and professional cyclists could not only be differentiated by age but also by anthropometric characteristics . Research has also shown differences in performance capacity between U23 and professional cyclists, such as the ability to minimise the downward shift in the power-duration curve and maximal aerobic power (van Erp, Sanders, et al. 2021;Maunder et al. 2021;Pinot and Grappe 2014). Previous research by Sallet et al. (2006) showed that in a non-fatigued state professional cyclists displayed improved gross mechanical efficiency compared to U23 cyclists (25.6±2.5 % vs. 24.4±2.0 ...
Monitoring and evaluating the physiological and performance characteristics of endurance athletes provides relevant information about the long-time athletic development, training process and talent identification. While there is growing evidence for the physiological and performance attributes in junior and professional cyclists, limited information is available about the U23 category. Therefore, the aim of this thesis was to examine the longitudinal physiological and performance characteristics of U23 elite cyclists, with a special focus on the application of the power profile and the power-duration relationship. Study 1 involved a critical evaluation of the current literature on power profiling methodologies and the application of the power-duration relationship. In order to improve the predictive ability of the power profile and the power-duration relationship across exercise intensity domains, it is recommended to ensure a high ecological validity (e.g. rider specialization, race demands) during standardized field testing. For this reason, single effort prediction trials outside the severe exercise intensity domain should be avoided, due to a high measurement bias and a low predictive ability regarding the power-duration relationship. Standardized field testing for power profiling should be conducted at least two times per season to obtain an accurate fingerprint of a cyclist’s performance capacity in the field. In addition, future research is required to better understand the fatigue mechanisms and downward-shift of the power profile and power-duration relationship in the moderate and heavy exercise intensity domains following prior heavy exercise. In Studies 2 and 3 the power profile and power-duration relationship were investigated throughout a competitive season in U23 elite cyclists. Study 2 examined the changes in maximal mean power output (MMP) and derived critical power (CP) and work capacity above critical power (W´) obtained during training and racing. The results revealed that the absolute power profile was not significantly different during a competitive season, except changes in the relative power profile due to a reduction in body mass. Study 3 investigated the differences in the power profile derived from training and racing, the training characteristics across a competitive season, and the relationships between the training characteristics and the power profile in U23 elite cyclists. Higher absolute and relative power profiles were recorded during racing than training. Training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season. Studies 4 and 5 analysed the power profile, workload characteristics and race performance in U23 and professional cyclists during a five-day multi-stage race. Study 4 compared the power profile, internal and external workloads, and racing performance between U23 and professional cyclists and between varying rider types, including allrounders, domestiques and general classification (GC) riders. This study demonstrated that the power profile after 1.000-3.000 kJ of total work could be used to evaluate the readiness of U23 cyclists to move into the professional ranks, as well as differentiate between rider types during racing. Study 5 specifically analysed climbing performance in a professional multistage race, and assessed the influence of climb category, prior workload, and intensity measures on climbing performance in U23 and professional cyclists. The findings indicated that climbing performance in professional road cycling is influenced by climb categorization as well as prior workload and intensity measures. Professional cyclists displayed better climbing performance than U23 cyclists, while the workload and intensity measures were higher in U23 than professional cyclists. Collectively the studies within this thesis have contributed to an improved understanding of the physiological and performance attributes of U23 elite cyclists in their maturation to the professional level. These studies have confirmed the practical application of the power profile and power-duration relationship for performance evaluation and prediction during training and racing. This thesis has enabled detailed insights about factors affecting the power profile and the power-duration relationship, and it has provided a concise applied strategy for the inclusion of power profiling in the longitudinal athletic development pathway to maximize cycling performance.
... Maximum performance capacity, i.e. the power profile of a cyclist, can be assessed in the field through the analysis of mean maximal power output over different durations . Several studies have descriptively analysed the power profile of elite/international level road cyclists during training and rac-ing Leo et al., 2021;Pinot & Grappe, 2011;Quod, Martin, Martin, & Laursen, 2010;Spragg, Leo, & Swart, 2022), but the underlying physiological mechanisms for an athlete's "durability" during prolonged endurance exercise are still debated (Maunder, Seiler, Mildenhall, Kilding, & Plews, 2021). ...
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Background This study aimed to investigate the impact of the intensity of prior accumulated work on the decline in power output in elite/international level road cyclists, comparing the effects of prior continuous moderate intensity versus intermittent high intensity cycling. Methods Nine elite/international level road cyclists (age 26.2 ± 4.0 years; body mass: 66.6 ± 5.5 kg; height: 176 ± 0.4 cm) conducted a 12-min field test (12 minfresh) during two consecutive training camps. Participants then performed both a 150-min moderate intensity continuous (MIC) work bout or a 150-min high intensity intermittent (HII) race simulation in randomized order, cross-over design. After each condition a 12-min field test (12 minfatigue) was completed. Results Absolute and relative 12 minfresh power output were not significantly different between training camps (p > 0.05). The 12 minfatigue power after HII was significantly lower than 12 minfatigue after MIC (∆ = 14 W; p = 0.014). Participants recorded more percentage time (%Time) in heart rate (HR) zone 3 (∆ = 9.2%; p = 0.003) and power output band between 5.0–7.9 W ⋅kg−1 (∆ = 8.9%; p = 0.002) as well as higher total work (∆ = 237 kJ; p ≤ 0.001) during HII. Conclusion These findings reveal that the decline in power output is higher after HII compared to MIC cycling work bouts. This suggests that the quantification of total work and intensity should be used in conjunction to predict a distinctive decline in power output. Future research is required to better understand the mechanisms of endurance “durability” in elite/international level road cyclists.
... In our case, improvements in maximal fat oxidation and/or movement economy might be the reason for the less severe decline in sport-specific performance. Especially during long rides, an improved "durability" in terms of an improved tolerance and less severe increase in heart rate during prolonged exercise were observed over the years which might be due to the high volumes of low-intensity training (62). ...
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Introduction: Paratriathlon allows competition for athletes with various physical impairments. The wheelchair category stands out from other paratriathlon categories, since competing in swimming, handcycling and wheelchair racing entails substantial demands on the upper-extremity. Hence, knowledge about exercise testing and training is needed to improve performance and avoid overuse injuries. We describe the training monitoring and performance development throughout a Paralympic cycle of an elite triathlete with spinal cord injury (SCI) and recent diagnosis of chronic myeloid leukemia (CML). Case Presentation/Methods: A 30-year-old wheelchair athlete with ten-year experience in wheelchair basketball contacted us for guidance regarding testing and training in paratriathlon. Laboratory and field tests were modified from protocols used for testing non-disabled athletes to examine his physical abilities. In handcycling, incremental tests were used to monitor performance development by means of lactate threshold (POBLA) and define heart rate-based training zones. All-out sprint tests were applied to calculate maximal lactate accumulation rate (V̇Lamax) as a measure of glycolytic capabilities in all disciplines. From 2017 to 2020, training was monitored to quantify training load (TL) and intensity distribution (TID). Results: From 2016 to 2019, the athlete was ranked within the top ten at European and World Championships. From 2017 to 2019, annual TL increased from 414 to 604 h and demonstrated a shift in TID from 77-17-6% to 88-8-4%. In this period, POBLA increased from 101 to 158 W and V̇Lamax decreased from 0.56 to 0.36 mmol·l-1·s-1. TL was highest during training camps. In 2020, after he received CML-diagnosis, TL, TID and POBLA were 317 h, 94-5-1%, and 108 W, respectively. Discussion: TL and TID demonstrated similar values when compared to previous studies in para swimming and long-distance paratriathlon, respectively. In contrast, relative TL during training camps exceeded those described in the literature and were accompanied by physical stress. Increased volumes at low-intensity are assumed to increase POBLA and decrease V̇Lamax over time. CML treatment and side-effects drastically decreased TL, intensity and performance which ultimately hindered a qualification for Tokyo 2020/21. In conclusion, there is need for careful training prescription and monitoring in wheelchair triathletes to improve performance and avoid non-functional overreaching.
... Athlete #1 had a baseline peak oxygen uptake during incremental running on a treadmill until exhaustion of 80 ml/min/kg, which can be considered rather good and comparable to that reported for endurance athletes, but not exceptional. A major determinant of endurance performance is not only peak oxygen uptake, but also the fraction that can be maintained for a prolonged time (Maunder et al., 2021). Athlete #1 was able to maintain 85% of his altitude adjusted HRmax, and presumably also a large fraction of his altitude adjusted aerobic power, throughout the race, even though some heart rate drift probably occurred (Mattson et al., 2011). ...
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Organized biannually in the Swiss Alps since 1984, the “Patrouille des Glaciers” (PDG) is one of the most challenging long-distance ski mountaineering (skimo) team competitions in the world. The race begins in Zermatt (1,616 m) and ends in Verbier (1,520 m), covering a total distance of 53 km with a cumulated 4,386 m of ascent and 4,482 m of descent. About 4,800 athletes take part in this competition, in teams of three. We hereby present the performance analysis of the uphill parts of this race of a member (#1) of the winning team in 2018, setting a new race record at 5 h and 35 min, in comparison with two amateur athletes. The athletes were equipped with the Global Navigation Satellite System (GNSS) antenna, a heart rate monitor, and a dedicated multisensor inertial measurement unit (IMU) attached to a ski, which recorded spatial-temporal gait parameters and transition events. The athletes' GNSS and heart rate data were synchronized with the IMU data. Athlete #1 had a baseline VO2 max of 80 ml/min/kg, a maximum heart rate of 205 bpm, weighed 69 kg, and had a body mass index (BMI) of 21.3 kg/m2. During the race, he carried 6 kg of gear and kept his heart rate constant around 85% of max. Spatiotemporal parameters analysis highlighted his ability to sustain higher power, higher pace, and, thus, higher vertical velocity than the other athletes. He made longer steps by gliding longer at each step and performed less kick turns in a shorter time. He spent only a cumulative 5 min and 30 s during skins on and off transitions. Skimo performance, thus, requires a high aerobic power of which a high fraction can be maintained for a prolonged time. Our results further confirm earlier observations that speed of ascent during endurance skimo competitions is a function of body weight and race gear and vertical energy cost of locomotion, with the latter function of climbing gradient. It is also the first study to provide some reference benchmarks for spatiotemporal parameters of elite and amateur skimo athletes during climbing using real-world data.
... The ability to produce high power outputs in a fatigued state has been termed 'fatigue resistance' (9) or preferably 'durability' (14). Interestingly research has suggested that durability differs between groups. ...
Results: Absolute 5MMPfatigue, 12MMPfatigue and relative 12MMPfatigue were significantly lower in late-season compared with early- and mid-season (p < 0.05). The difference in absolute 12MMPfresh and 12MMPfatigue was significantly greater in late than in early- and mid-season.A significant relationship was found between training time below the first ventilatory threshold (Time < VT1) and improvements in absolute and relative 2MMPfatigue (r = 0.43 p = 0.018 and r = 0.376 p = 0.04 respectively); and between a shift towards a polarised training intensity distribution and improvements in absolute and relative 12MMPfatigue (r = 0.414p = 0.023 for both) between subsequent periods. Conclusion: There is greater variability in the fatigue power profile across a competitive season than the fresh power profile.
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The requirements of running a 2 hour marathon have been extensively debated but the actual physiological demands of running at ~21.1 km/h have never been reported. We therefore conducted laboratory-based physiological evaluations and measured running economy (O2 cost) while running outdoors at ~21.1 km/h, in world-class distance runners as part of Nike's 'Breaking 2' marathon project. On separate days, 16 male distance runners (age, 29 ± 4 years; height, 1.72 ± 0.04 m; mass, 58.9 ± 3.3 kg) completed an incremental treadmill test for the assessment of V̇O2peak, O2 cost of submaximal running, lactate threshold and lactate turn-point, and a track test during which they ran continuously at 21.1 km/h. The laboratory-determined V̇O2peak was 71.0 ± 5.7 ml/kg/min with lactate threshold and lactate turn-point occurring at 18.9 ± 0.4 and 20.2 ± 0.6 km/h, corresponding to 83 ± 5 % and 92 ± 3 % V̇O2peak, respectively. Seven athletes were able to attain a steady-state V̇O2 when running outdoors at 21.1 km/h. The mean O2 cost for these athletes was 191 ± 19 ml/kg/km such that running at 21.1 km/h required an absolute V̇O2 of ~4.0 L/min and represented 94 ± 3 % V̇O2peak. We report novel data on the O2 cost of running outdoors at 21.1 km/h, which enables better modelling of possible marathon performances by elite athletes. Using the value for O2 cost measured in this study, a sub-2 hour marathon would require a 59 kg runner to sustain a V̇O2 of approximately 4.0 L/min or 67 ml/kg/min.
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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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Introduction: Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons. Methods: We investigated running activities logged into an online fitness platform by >25,000 runners prior to big-city marathons. Each activity contained time, distance, and elevation every 100 m. We computed grade-adjusted pacing and the fastest pace recorded for a set of target distances (400, 800, 1000, 1500, 3000, 5000 m). CS was determined as the slope of the distance-time relationship using all combinations of, at least, three target distances. Results: The relationship between distance and time was linear, irrespective of the target distances used (pooled mean ± standard deviation: R = 0.9999±0.0001). The estimated values of CS from all models were not different (3.74±0.08 m·s), and all models correlated with marathon performance (R = 0.672±0.036, error = 8.01±0.51%). CS from the model including 400, 800 and 5000 m best predicted performance (R = 0.695, error = 7.67%), and was used in further analysis. Runners completed the marathon at 84.8±13.6% CS, with faster runners competing at speeds closer to CS (93.0% CS for 150 min marathon times vs. 78.9% CS for 360 min marathon times). Runners who completed the first half of the marathon at >94% of their CS, and particularly faster than CS, were more likely slowdown by more than 25% in the second half of race. Conclusion: This study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.
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We reinforce the key messages in our earlier review paper that critical power, rather than maximal lactate steady state, provides the better index for defining steady‐state vs non‐steady state physiological behaviour during exercise.
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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.
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.
The anaerobic threshold (AT) remains a widely recognized, and contentious, concept in exercise physiology and medicine. As conceived by Karlman Wasserman, the AT coalesced the increase of blood lactate concentration ([La- ]), during a progressive exercise test, with an excess pulmonary carbon dioxide output ( V ̇ C O 2 ). Its principal tenets were: limiting oxygen (O2 ) delivery to exercising muscle→increased glycolysis, La- and H+ production→decreased muscle and blood pH→with increased H+ buffered by blood [HCO3- ]→increased CO2 release from blood→increased V ̇ C O 2 and pulmonary ventilation. This schema stimulated scientific scrutiny which challenged the fundamental premise that muscle anoxia was requisite for increased muscle and blood [La- ]. It is now recognized that insufficient O2 is not the primary basis for lactataemia. Increased production and utilization of La- represent the response to increased glycolytic flux elicited by increasing work rate, and determine the oxygen uptake ( V ̇ O 2 ) at which La- accumulates in the arterial blood (the lactate threshold; LT). However, the threshold for a sustained non-oxidative contribution to exercise energetics is the critical power, which occurs at a metabolic rate often far above the LT and separates heavy from very heavy/severe-intensity exercise. Lactate is now appreciated as a crucial energy source, major gluconeogenic precursor and signalling molecule but there is no ipso facto evidence for muscle dysoxia or anoxia. Non-invasive estimation of LT using the gas exchange threshold (non-linear increase of V ̇ C O 2 versus V ̇ O 2 ) remains important in exercise training and in the clinic, but its conceptual basis should now be understood in light of lactate shuttle biology.
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.
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.