Validation of a novel intermittent w' model for cycling using field data

Individualized physiology-based digital twin model for sports performance prediction: a reinterpretation of the Margaria-Morton model

Performance in many racing sports depends on the ability of the athletes to produce and maintain the highest possible work i.e., the highest power for the duration of the race. To model this energy production in an individualized way, an adaptation and a reinterpretation (including a physiological meaning of parameters) of the three-component Margaria-Morton model were performed. The model is applied to the muscles involved in a given task. The introduction of physiological meanings was possible thanks to the measurement of physiological characteristics for a given athlete. A method for creating a digital twin was therefore proposed and applied for national-level cyclists. The twins thus created were validated by comparison with field performance, experimental observations, and literature data. Simulations of record times and 3-minute all-out tests were consistent with experimental data. Considering the literature, the model provided good estimates of the time course of muscle metabolite concentrations (e.g., lactate and phosphocreatine). It also simulated the behavior of oxygen kinetics at exercise onset and during recovery. This methodology has a wide range of applications, including prediction and optimization of the performance of individually modeled athletes.

A dynamic model of the bi-exponential reconstitution and expenditure of W' in trained cyclists

ABSTRACTThe aim of this study was to investigate the effects of different recovery power outputs on the reconstitution of W' and to develop a dynamic bi-exponential model of W' during depletion and reconstitution. Ten trained cyclists (mass 71.7 ± 8.4 kg; V̇O2max 60.0 ± 6.3 ml·kg-1·min-1) completed three incremental ramps (20 W·min-1) to the limit of tolerance on each of six occasions with recovery durations of 30 and 240 s. Recovery power outputs varied between 50 W (LOW); 60% of critical power (CP) (MOD) and 85% of CP (HVY). W' reconstitution was measured following each recovery and fitted to a bi-exponential model. Amplitude and time constant (τ) parameters were then determined via regression analysis accounting for relative intensity and duration to produce a dynamic model of W'. W' reconstitution slowed disproportionately as recovery power output increased (p < 0.001) and increased with recovery duration (p < 0.001). The amplitudes of each recovery component were strongly correlated to W' reconstitution after 240 s at HVY (r = 0.95), whilst τ parameters were found to be related to the fractional difference between recovery power and CP. The predictive capacity of the resultant model was assessed against experimental data with no differences found between predicted and experimental values of W' reconstitution (p > 0.05). The dynamic bi-exponential model of W' accounting for varying recovery intensities closely described W' kinetics in trained cyclists facilitating real-time decisions about pacing and tactics during competition. The model can be customised for individuals from known CP and W' and a single additional test session.HighlightsA dynamic bi-exponential model of W' accounting for both varying power output and duration.Individual customisation of the model can be achieved with a single specific test session.W' reconstitution slows disproportionally with increasing intensity after repeated bouts.

Modeling the expenditure and reconstitution of distance above critical speed during two swimming interval training sessions

In swimming, the speed-time relationship provides the critical speed (CS) and the maximum distance that can be performed above CS (D′). During intermittent severe intensity exercise, a complete D′ depletion coincides with task failure, while a sub-CS intensity is required for D′ reconstitution. Therefore, determining the balance D′ remaining at any time during intermittent exercise (D'_BAL) could improve training prescription. This study aimed to 1) test the D'_BAL model for swimming; 2) determine an equation to estimate the time constant of the reconstitution of D' (τD′); and 3) verify if τD′ is constant during two interval training sessions with the same work intensity and duration and recovery intensity, but different recovery duration. Thirteen swimmers determined CS and D′ and performed two high-intensity interval sessions at a constant speed, with repetitions fixed at 50 m. The duration of passive recovery was based on the work/relief ratio of 2:1 (T2:1) and 4:1 (T4:1). There was a high variability between sessions for τD' (coefficient of variation of 306%). When τD′ determined for T2:1 was applied in T4:1 and vice versa, the D'_BAL model was inconsistent to predict the time to exhaustion (coefficient of variation of 29 and 28%). No linear or nonlinear relationships were found between τD′ and CS, possibly due to the high within-subject variability of τD'. These findings suggest that τD′ is not constant during two high-intensity interval sessions with the same recovery intensity. Therefore, the current D'_BAL model was inconsistent to track D′ responses for swimming sessions tested herein.

Can Popular High-Intensity Interval Training (HIIT) Models Lead to Impossible Training Sessions?

High-Intensity Interval Training (HIIT) is a time-efficient training method suggested to improve health and fitness for the clinical population, healthy subjects, and athletes. Many parameters can impact the difficulty of HIIT sessions. This study aims to highlight and explain, through logical deductions, some limitations of the Skiba and Coggan models, widely used to prescribe HIIT sessions in cycling. We simulated 6198 different HIIT training sessions leading to exhaustion, according to the Skiba and Coggan-Modified (modification of the Coggan model with the introduction of an exhaustion criterion) models, for three fictitious athlete profiles (Time-Trialist, All-Rounder, Sprinter). The simulation revealed impossible sessions (i.e., requiring athletes to surpass their maximal power output over the exercise interval duration), characterized by a few short exercise intervals, performed in the severe and extreme intensity domains, alternating with long recovery bouts. The fraction of impossible sessions depends on the athlete profile and ranges between 4.4 and 22.9% for the Skiba model and 0.6 and 3.2% for the Coggan-Modified model. For practitioners using these HIIT models, this study highlights the importance of understanding these models’ inherent limitations and mathematical assumptions to draw adequate conclusions from their use to prescribe HIIT sessions.

Bi-exponential modelling of [Formula: see text] reconstitution kinetics in trained cyclists

PURPOSE

The aim of this study was to investigate the individual [Formula: see text] reconstitution kinetics of trained cyclists following repeated bouts of incremental ramp exercise, and to determine an optimal mathematical model to describe [Formula: see text] reconstitution.

METHODS

Ten trained cyclists (age 41 ± 10 years; mass 73.4 ± 9.9 kg; [Formula: see text] 58.6 ± 7.1 mL kg min^-1) completed three incremental ramps (20 W min^-1) to the limit of tolerance with varying recovery durations (15-360 s) on 5-9 occasions. [Formula: see text] reconstitution was measured following the first and second recovery periods against which mono-exponential and bi-exponential models were compared with adjusted R² and bias-corrected Akaike information criterion (AICc).

RESULTS

A bi-exponential model outperformed the mono-exponential model of [Formula: see text] reconstitution (AICc 30.2 versus 72.2), fitting group mean data well (adjR² = 0.999) for the first recovery when optimised with parameters of fast component (FC) amplitude = 50.67%; slow component (SC) amplitude = 49.33%; time constant (τ)_FC = 21.5 s; τ_SC = 388 s. Following the second recovery, W' reconstitution reduced by 9.1 ± 7.3%, at 180 s and 8.2 ± 9.8% at 240 s resulting in an increase in the modelled τ_SC to 716 s with τ_FC unchanged. Individual bi-exponential models also fit well (adjR² = 0.978 ± 0.017) with large individual parameter variations (FC amplitude 47.7 ± 17.8%; first recovery: (τ)_FC = 22.0 ± 11.8 s; (τ)_SC = 377 ± 100 s; second recovery: (τ)_FC = 16.3.0 ± 6.6 s; (τ)_SC = 549 ± 226 s).

CONCLUSIONS

W' reconstitution kinetics were best described by a bi-exponential model consisting of distinct fast and slow phases. The amplitudes of the FC and SC remained unchanged with repeated bouts, with a slowing of W' reconstitution confined to an increase in the time constant of the slow component.

Interaction of exercise bioenergetics with pacing behavior predicts track distance running performance

The best possible finishing time for a runner competing in distance track events can be estimated from their critical speed (CS) and the finite amount of energy that can be expended above CS (D´). During tactical races with variable pacing, the runner with the "best" combination of CS and D´ and, therefore, the fastest estimated finishing time prior to the race, does not always win. We hypothesized that final race finishing positions depend on the relationships between the pacing strategies used, the athletes' initial CS, and their instantaneous D´ (i.e., D´ balance) as the race unfolds. Using publicly available data from the 2017 International Association of Athletics Federations (IAAF) World Championships men's 5,000-m and 10,000-m races, race speed, CS, and D´ balance were calculated. The correlation between D´ balance and actual finishing positions was nonsignificant using start-line values but improved to R² > 0.90 as both races progressed. The D´ balance with 400 m remaining was strongly associated with both final 400-m split time and proximity to the winner. Athletes who exhausted their D´ were unable to hold pace with the leaders, whereas a high D´ remaining enabled a fast final 400 m and a high finishing position. The D´ balance model was able to accurately predict finishing positions in both a "slow" 5,000-m and a "fast" 10,000-m race. These results indicate that although CS and D´ can characterize an athlete's performance capabilities prior to the start, the pacing strategy that optimizes D´ utilization significantly impacts the final race outcome.NEW & NOTEWORTHY We show that the interaction between exercise bioenergetics and real-time pacing strategy predicts track distance running performance. Critical speed (CS) and the finite energy expended above CS (D´) can characterize an athlete's capabilities prior to the race start, but the pacing strategy that optimizes D´ utilization ultimately impacts whether a runner is in contention to win and whether a runner will have a fast final 400 m. Accordingly, D´ balance predicts final race finishing order.

The W' Balance Model: Mathematical and Methodological Considerations

Since its publication in 2012, the W' balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model's intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W' balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W' during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W' balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise.

Validating an Adjustment to the Intermittent Critical Power Model for Elite Cyclists-Modeling W' Balance During World Cup Team Pursuit Performances

PURPOSE

Modeling intermittent work capacity is an exciting development to the critical power model with many possible applications across elite sport. With the Skiba 2 model validated using subelite participants, an adjustment to the model's recovery rate has been proposed for use in elite cyclists (Bartram adjustment). The team pursuit provides an intermittent supramaximal event with which to validate the modeling of W' in this population.

METHODS

Team pursuit data of 6 elite cyclists competing for Australia at a Track World Cup were solved for end W' values using both the Skiba 2 model and the Bartram adjustment. Each model's success was evaluated by its ability to approximate end W' values of 0 kJ, as well as a count of races modeled to within a predetermined error threshold of ±1.840 kJ.

RESULTS

On average, using the Skiba 2 model found end W' values different from zero (P = .007; mean ± 95% confidence limit, -2.7 ± 2.0 kJ), with 3 out of 8 cases ending within the predetermined error threshold. Using the Bartram adjustment on average resulted in end W' values that were not different from zero (P = .626; mean ± 95% confidence limit, 0.5 ± 2.5 kJ), with 4 out of 8 cases falling within the predetermined error threshold.

CONCLUSIONS

On average, the Bartram adjustment was an improvement to modeling intermittent work capacity in elite cyclists, with the Skiba 2 model underestimating the rate of W' recovery. In the specific context of modeling team pursuit races, all models were too variable for effective use; hence, individual recovery rates should be explored beyond population-specific rates.

W' Recovery Kinetics after Exhaustion: A Two-Phase Exponential Process Influenced by Aerobic Fitness

PURPOSE

The aims of this study were 1) to model the temporal profile of W' recovery after exhaustion, 2) to estimate the contribution of changing V˙O2 kinetics to this recovery, and 3) to examine associations with aerobic fitness and muscle fiber type (MFT) distribution.

METHODS

Twenty-one men (age = 25 ± 2 yr, V˙O2peak = 54.4 ± 5.3 mL·min-1·kg-1) performed several constant load tests to determine critical power and W' followed by eight trials to quantify W' recovery. Each test consisted of two identical exhaustive work bouts (WB1 and WB2), separated by a variable recovery interval of 30, 60, 120, 180, 240, 300, 600, or 900 s. Gas exchange was measured and muscle biopsies were collected to determine MFT distribution. W' recovery was quantified as observed W' recovery (W'OBS), model-predicted W' recovery (W'BAL), and W' recovery corrected for changing V˙O2 kinetics (W'ADJ). W'OBS and W'ADJ were modeled using mono- and biexponential fitting. Root-mean-square error (RMSE) and Akaike information criterion (∆AICC) were used to evaluate the models' accuracy.

RESULTS

The W'BAL model (τ = 524 ± 41 s) was associated with an RMSE of 18.6% in fitting W'OBS and underestimated W' recovery for all durations below 5 min (P < 0.002). Monoexponential modeling of W'OBS resulted in τ = 104 s with RMSE = 6.4%. Biexponential modeling of W'OBS resulted in τ1 = 11 s and τ2 = 256 s with RMSE = 1.7%. W'ADJ was 11% ± 1.5% lower than W'OBS (P < 0.001). ∆AICC scores favored the biexponential model for W'OBS, but not for W'ADJ. V˙O2peak (P = 0.009) but not MFT distribution (P = 0.303) was associated with W'OBS.

CONCLUSION

We showed that W' recovery from exhaustion follows a two-phase exponential time course that is dependent on aerobic fitness. The appearance of a fast initial recovery phase was attributed to an enhanced aerobic energy provision resulting from changes in V˙O2 kinetics.

The balance of muscle oxygen supply and demand reveals critical metabolic rate and predicts time to exhaustion

We tested the hypothesis that during whole body exercise, the balance between muscle O₂ supply and metabolic demand may elucidate intensity domains, reveal a critical metabolic rate, and predict time to exhaustion. Seventeen active, healthy volunteers (12 males, 5 females; 32 ± 2 yr) participated in two distinct protocols. Study 1 (n = 7) consisted of constant work rate cycling in the moderate, heavy, and severe exercise intensity domains with concurrent measures of pulmonary V̇o₂ and local %SmO₂ [via near-infrared spectroscopy (NIRS)] on quadriceps and forearm sites. Average %SmO₂ at both sites displayed a domain-dependent response (P < 0.05). A negative %SmO₂ slope was evident during severe-domain exercise but was positive during exercise below critical power (CP) at both muscle sites. In study 2 (n = 10), quadriceps and forearm site %SmO₂ was measured during three continuous running trials to exhaustion and three intermittent intensity (ratio = 60 s severe: 30 s lower intensity) trials to exhaustion. Intensity-dependent negative %SmO₂ slopes were observed for all trials (P < 0.05) and predicted zero slope at critical velocity. %SmO₂ accurately predicted depletion and repletion of %D' balance on a second-by-second basis (R² = 0.99, P < 0.05; both sites). Time to exhaustion predictions during continuous and intermittent exercise were either not different or better with %SmO₂ [standard error of the estimate (SEE) < 20.52 s for quad, <44.03 s for forearm] versus running velocity (SEE < 65.76 s). Muscle O₂ balance provides a dynamic physiological delineation between sustainable and unsustainable exercise (consistent with a "critical metabolic rate") and predicts real-time depletion and repletion of finite work capacity and time to exhaustion.NEW & NOTEWORTHY Dynamic muscle O₂ saturation discriminates boundaries between exercise intensity domains, exposes a critical metabolic rate as the highest rate of steady state O₂ supply and demand, describes time series depletion and repletion for work above critical power, and predicts time to exhaustion during severe domain whole body exercise. These results highlight the matching of O₂ supply and demand as a primary determinant for sustainable exercise intensities from those that are unsustainable and lead to exhaustion.

Using Field Based Data to Model Sprint Track Cycling Performance

Cycling performance models are used to study rider and sport characteristics to better understand performance determinants and optimise competition outcomes. Performance requirements cover the demands of competition a cyclist may encounter, whilst rider attributes are physical, technical and psychological characteristics contributing to performance. Several current models of endurance-cycling enhance understanding of performance in road cycling and track endurance, relying on a supply and demand perspective. However, they have yet to be developed for sprint-cycling, with current athlete preparation, instead relying on measures of peak-power, speed and strength to assess performance and guide training. Peak-power models do not adequately explain the demands of actual competition in events over 15-60 s, let alone, in World-Championship sprint cycling events comprising several rounds to medal finals. Whilst there are no descriptive studies of track-sprint cycling events, we present data from physiological interventions using track cycling and repeated sprint exercise research in multiple sports, to elucidate the demands of performance requiring several maximal sprints over a competition. This review will show physiological and power meter data, illustrating the role of all energy pathways in sprint performance. This understanding highlights the need to focus on the capacity required for a given race and over an event, and therefore the recovery needed for each subsequent race, within and between races, and how optimal pacing can be used to enhance performance. We propose a shift in sprint-cyclist preparation away from training just for peak power, to a more comprehensive model of the actual event demands.

Oxygen Demand, Uptake, and Deficits in Elite Cross-Country Skiers during a 15-km Race

PURPOSE

This study aimed to quantify the repeated oxygen deficits attained during intermittent endurance exercise by measuring oxygen consumption (V˙O2) and oxygen demand (V˙O2) throughout a simulated roller ski race.

METHODS

Eight male elite cross-country skiers (V˙O2peak, 77.4 ± 4.4 mL·kg⋅min) raced a 13.5-km roller ski time trial on a World Cup course. On two additional days, athletes completed (i) six submaximal loads (~5 min) and ~4-min maximal trial to establish athlete-specific estimates of skiing economy, V˙O2peak, and maximal ΣO2 (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 V˙O2 was measured breath by breath. V˙O2 and ΣO2 were calculated using an athlete-specific model for skiing economy throughout the treadmill simulation.

RESULTS

During the treadmill simulation, V˙O2 was on average 0.77 V˙O2peak, and active V˙O2 (i.e., excluding the time in simulated downhill) was on average 1.01 V˙O2peak. The athletes repeatedly attained substantial oxygen deficits in individual uphill sections of the treadmill simulation, but the deficits were typically small compared with their MAOD (average 14%, range ~0%-50%). However, the ΣO2 summed over all periods of active propulsion was on average 3.8 MAOD.

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' MAOD. This suggests that the 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.

The Application of Critical Power, the Work Capacity above Critical Power (W'), and its Reconstitution: A Narrative Review of Current Evidence and Implications for Cycling Training Prescription

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.

Field-Derived Power-Duration Variables to Predict Cycling Time-Trial Performance

PURPOSE

To evaluate the predictive validity of critical power (CP) and the work above CP (W') on cycling performance (mean power during a 20-min time trial; TT20).

METHODS

On 3 separate days, 10 male cyclists completed a TT20 and 3 CP and W' prediction trials of 1, 4, and 10 min and 2, 7, and 12 min in field conditions. CP and W' were modeled across combinations of these prediction trials with the hyperbolic, linear work/time, and linear power inverse-time (INV) models. The agreement and the uncertainty between the predicted and actual TT20 were assessed with 95% limits of agreement and a probabilistic approach, respectively.

RESULTS

Differences between the predicted and actual TT20 were "trivial" for most of the models if the 1-min trial was not included. Including the 1-min trial in the INV and linear work/time models "possibly" to "very likely" overestimated TT20. The INV model provided the smallest total error (ie, best individual fit; 6%) for all cyclists (305 [33] W; 19.6 [3.6] kJ). TT20 predicted from the best individual fit-derived CP, and W' was strongly correlated with actual TT20 (317 [33] W; r = .975; P < .001). The bias and 95% limits of agreement were 4 (7) W (-11 to 19 W).

CONCLUSIONS

Field-derived CP and W' accurately predicted cycling performance in the field. The INV model was most accurate to predict TT20 (1.3% [2.4%]). Adding a 1-min-prediction trial resulted in large total errors, so it should not be included in the models.

High-intensity decreasing interval training (HIDIT) increases time above 90% [Formula: see text]O2peak

Purpose

Training near \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂max is considered to be the most effective way to enhance \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂max. High-intensity interval training (HIIT) is a well-known time-efficient training method for improving cardiorespiratory and metabolic function and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂max. While long HIIT bouts allow \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂max to be achieved quickly, short HIIT bouts improve time to exhaustion (Tlim). The aim of this study was to evaluate the time spent above 90% \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂peak (T > 90% \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂peak) during three different HIIT protocols.

Methods

Twelve cyclists performed three HIIT sessions. Each protocol had the same work and recovery power and ratio of work·recovery⁻¹. The protocols consisted of long-interval HIIT (LI_HIIT, 3 min work—2 min recovery), short-interval HIIT (SI_HIIT, 30 s work—20 s recovery), and high-intensity decreasing interval training (HIDIT, work from 3 min to 30 s and recovery from 2 min to 20 s). T > 90% \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂peak, Tlim, blood lactate [La], and rate of perceived exertion (RPE) were measured at Tlim.

Results

T > 90% \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂peak was greater in HIDIT (312 ± 207 s) than in SI_HIIT (182 ± 225 s; P = 0.036) or LI_HIIT (179 ± 145 s; P = 0.027). Tlim was not significantly different (P > 0.05) between HIDIT (798 ± 185 s), SI_HIIT (714 ± 265 s), and LI_HIIT (664 ± 282). At Tlim, no differences in [La] and RPE were found between protocols (P > 0.05).

Conclusion

HIDIT showed the highest T > 90% \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂peak, suggesting that it may be a good strategy to increase time close to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O₂peak, despite similar Tlim, [La], and RPE at Tlim.

A survey of mathematical models of human performance using power and energy

The ability to predict the systematic decrease of power during physical exertion gives valuable insights into health, performance, and injury. This review surveys the research of power-based models of fatigue and recovery within the area of human performance. Upon a thorough review of available literature, it is observed that the two-parameter critical power model is most popular due to its simplicity. This two-parameter model is a hyperbolic relationship between power and time with critical power as the power-asymptote and the curvature constant denoted by W′. Critical power (CP) is a theoretical power output that can be sustained indefinitely by an individual, and the curvature constant (W′) represents the amount of work that can be done above CP. Different methods and models have been validated to determine CP and W′, most of which are algebraic manipulations of the two-parameter model. The models yield different CP and W′ estimates for the same data depending on the regression fit and rounding off approximations. These estimates, at the subject level, have an inherent day-to-day variability called intra-individual variability (IIV) associated with them, which is not captured by any of the existing methods. This calls for a need for new methods to arrive at the IIV associated with CP and W′. Furthermore, existing models focus on the expenditure of W′ for efforts above CP and do not model its recovery in the sub-CP domain. Thus, there is a need for methods and models that account for (i) the IIV to measure the effectiveness of individual training prescriptions and (ii) the recovery of W′ to aid human performance optimization.

Energy system contribution during competitive cross-country skiing

Energy system contribution during cross-country (XC) skiing races is dependent on several factors, including the race duration, track profile, and sub-techniques applied, and their subsequent effects on the use of the upper and lower body. This review provides a scientific synopsis of the interactions of energy system contributions from a physiological, technical, and tactical perspective. On average, the aerobic proportion of the total energy expended during XC skiing competitions is comparable to the values for other sports with similar racing times. However, during both sprint (≤ 1.8 km) and distance races (≥ 10 and 15 km, women and men, respectively) a high aerobic turnover interacts with subsequent periods of very high work rates at ~ 120 to 160% of VO_2peak during the uphill sections of the race. The repeated intensity fluctuations are possible due to the nature of skiing, which involves intermittent downhills where skiers can recover. Thus, the combination of high and sustained aerobic energy turnover and repeated work rates above VO_2peak, interspersed with short recovery periods, distinguishes XC skiing from most other endurance sports. The substantially increased average speed in races over recent decades, frequent competitions in mass starts and sprints, and the greater importance of short periods at high speeds in various sub-techniques, have demanded changes in the physiological, technical, and tactical abilities needed to achieve world-class level within the specific disciplines.

The maximal metabolic steady state: redefining the 'gold standard'

The maximal lactate steady state (MLSS) and the critical power (CP) are two widely used indices of the highest oxidative metabolic rate that can be sustained during continuous exercise and are often considered to be synonymous. However, while perhaps having similarities in principle, methodological differences in the assessment of these parameters typically result in MLSS occurring at a somewhat lower power output or running speed and exercise at CP being sustainable for no more than approximately 20-30 min. This has led to the view that CP overestimates the 'actual' maximal metabolic steady state and that MLSS should be considered the 'gold standard' metric for the evaluation of endurance exercise capacity. In this article we will present evidence consistent with the contrary conclusion: i.e., that (1) as presently defined, MLSS naturally underestimates the actual maximal metabolic steady state; and (2) CP alone represents the boundary between discrete exercise intensity domains within which the dynamic cardiorespiratory and muscle metabolic responses to exercise differ profoundly. While both MLSS and CP may have relevance for athletic training and performance, we urge that the distinction between the two concepts/metrics be better appreciated and that comparisons between MLSS and CP, undertaken in the mistaken belief that they are theoretically synonymous, is discontinued. CP represents the genuine boundary separating exercise in which physiological homeostasis can be maintained from exercise in which it cannot, and should be considered the gold standard when the goal is to determine the maximal metabolic steady state.

Slowing the Reconstitution of W' in Recovery With Repeated Bouts of Maximal Exercise

PURPOSE

This study examined the partial reconstitution of the work capacity above critical power (W') following successive bouts of maximal exercise using a new repeated ramp test, against which the fit of an existing W' balance ( Wbal' ) prediction model was tested.

METHODS

Twenty active adults, consisting of trained cyclists (n = 9; age 43 [15] y, V˙O2max 61.9 [8.5] mL·kg^-1·min^-1) and untrained cyclists (n = 11; age 36 [15] y, V˙O2max 52.4 [5.8] mL·kg^-1·min^-1) performed 2 tests 2 to 4 d apart, consisting of 3 incremental ramps (20 W·min^-1) to exhaustion interspersed with 2-min recoveries.

RESULTS

Intratrial differences between recoveries demonstrated significant reductions in the amount of W' reconstituted for the group and both subsets (P < .05). The observed minimal detectable changes of 475 J (first recovery) and 368 J (second recovery) can be used to monitor changes in the rate of W' reconstitution in individual trained cyclists. Intertrial relative reliability of W' reconstitution was evaluated by intraclass correlation coefficients for the group (≥.859) and the trained (≥.940) and untrained (≥.768) subsets. Absolute reliability was evaluated with typical error (TE) and coefficient of variation (CV) for the group (TE ≤ 559 J, CV ≤ 9.2%), trained (TE ≤ 301 J, CV ≤ 4.7%), and untrained (TE ≤ 720 J, CV ≤ 12.4%).

CONCLUSIONS

The reconstitution of W' is subject to a fatiguing effect hitherto unaccounted for in Wbal' prediction models. Furthermore, the Wbal' model did not provide a good fit for the repeated ramp test, which itself proved to be a reliable test protocol.

Time of VO(2)max plateau and post-exercise oxygen consumption during incremental exercise testing in young mountain bike and road cyclists

The purpose of this study was to compare markers of glycolytic metabolism in response to the Wingate test and the incremental test in road and mountain bike cyclists, who not different performance level and aerobic capacity. All cyclists executed the Wingate test and incremental test on a cycle ergometer. Maximal power and average power were determined during the Wingate test. During the incremental test the load was increased by 50 W every 3 min, until volitional exhaustion and maximal aerobic power (APmax), maximal oxygen uptake (VO2max), and time of VO(2)max plateau (Tplateau) were determined. Post-exercise measures of oxygen uptake (VO(2)post), carbon dioxide excretion, (VCO(2)post), and the ratio between VCO(2)/VO(2) (RERpost) were collected for 3 min immediately after incremental test completion. Arterialized capillary blood was drawn to measure lactate (La-) and hydrogen (H+) ion concentrations in 3 min after each test. The data demonstrated significant differences between mountain bike and road cyclists for Tplateau, VO(2)post, VCO(2)post, La- which was higher-, and RERpost which was lower-, in mountain bike cyclists compare with road cyclists. No differences were observed between mountain bike and road cyclists for APmax, VO(2)max, H(+) and parameters measured in the Wingate test. Increased time of VO2max plateau concomitant to larger post-exercise La- and VO(2) values suggests greater anaerobic contribution during incremental testing efforts by mountain bike cyclists compared with road cyclists.

Critical Velocity during Intermittent Running with Changes of Direction

PURPOSE

We tested the hypothesis that critical velocity (CV) during intermittent running with changes of direction is reliably and accurately identified from a simple shuttle field test. We also tested the hypothesis that CV during intermittent running with changes of direction running is not equivalent to continuous linear running.

METHODS

Young adults performed a custom shuttle test of intermittent sprint running to reveal CV. Sprints were 18.3 m per direction, with rest between sprints of 15 s for 3 min, 10 s for 2 min, and no rest for 2 min (7 min total). To test reliability, the CV shuttle test (CVST) was performed twice. To test validity, blood lactate was assessed during two separate trials inclusive of 5% above or below CVST end velocity. To explore task specificity, CV during CVST was compared to CV obtained from three linear running time trials.

RESULTS

Total distance and CSVT end test velocity were similar between visits (864 ± 21 m and 3.23 ± 0.13 m·s vs 900 ± 30 m and 3.21 ± 0.15 m·s, respectively). At 5% above CVST end velocity, all subjects failed to complete 20 min and had unstable blood lactate values. A steady state blood lactate profile was observed during trials 5% below end velocity and all subjects completed the trial. The CV from the CVST was lower than the CV from linear running (△ -17% ± 6%), highlighting the importance of test specificity for threshold determination.

CONCLUSIONS

The CVST provides a reliable and accurate determination of CV and can be used by coaches, athletes, and trainers to better understand the physiological impact specific to practice or competitions involving intermittent change of direction running.

Accuracy of W' Recovery Kinetics in High Performance Cyclists-Modeling Intermittent Work Capacity

PURPOSE

With knowledge of an individual's critical power and W', the SKIBA 2 model provides a framework with which to track W' balance during intermittent high-intensity work bouts. There are fears that the time constant controlling the recovery rate of W' (τ_W') may require refinement to enable effective use in an elite population.

METHODS

Four elite endurance cyclists completed an array of intermittent exercise protocols to volitional exhaustion. Each protocol lasted approximately 3.5-6 min and featured a range of recovery intensities, set in relation to the athlete's critical power (D_CP). Using the framework of the SKIBA 2 model, the τ_W' values were modified for each protocol to achieve an accurate W' at volitional exhaustion. Modified τ_W' values were compared with equivalent SKIBA 2 τ_W' values to assess the difference in recovery rates for this population. Plotting modified τ_W' values against D_CP showed the adjusted relationship between work rate and recovery rate.

RESULTS

Comparing modified τ_W' values against the SKIBA 2 τ_W' values showed a negative bias of 112 (46) s (mean ± 95% confidence limits), suggesting that athletes recovered W' faster than predicted by SKIBA 2 (P = .0001). The modified τ_W'-D_CP relationship was best described by a power function: τ_W' = 2287.2 × D_CP^-0.688 (R² = .433).

CONCLUSIONS

The current SKIBA 2 model is not appropriate for use in elite cyclists, as it underpredicts the recovery rate of W'. The modified τ_W' equation presented will require validation but appears more appropriate for high-performance athletes. Individual τ_W' relationships may be necessary to maximize the model's validity.

The Critical Power Model as a Potential Tool for Anti-doping

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.

The 'Critical Power' Concept: Applications to Sports Performance with a Focus on Intermittent High-Intensity Exercise

The curvilinear relationship between power output and the time for which it can be sustained is a fundamental and well-known feature of high-intensity exercise performance. This relationship 'levels off' at a 'critical power' (CP) that separates power outputs that can be sustained with stable values of, for example, muscle phosphocreatine, blood lactate, and pulmonary oxygen uptake ([Formula: see text]), from power outputs where these variables change continuously with time until their respective minimum and maximum values are reached and exercise intolerance occurs. The amount of work that can be done during exercise above CP (the so-called W') is constant but may be utilized at different rates depending on the proximity of the exercise power output to CP. Traditionally, this two-parameter CP model has been employed to provide insights into physiological responses, fatigue mechanisms, and performance capacity during continuous constant power output exercise in discrete exercise intensity domains. However, many team sports (e.g., basketball, football, hockey, rugby) involve frequent changes in exercise intensity and, even in endurance sports (e.g., cycling, running), intensity may vary considerably with environmental/course conditions and pacing strategy. In recent years, the appeal of the CP concept has been broadened through its application to intermittent high-intensity exercise. With the assumptions that W' is utilized during work intervals above CP and reconstituted during recovery intervals below CP, it can be shown that performance during intermittent exercise is related to four factors: the intensity and duration of the work intervals and the intensity and duration of the recovery intervals. However, while the utilization of W' may be assumed to be linear, studies indicate that the reconstitution of W' may be curvilinear with kinetics that are highly variable between individuals. This has led to the development of a new CP model for intermittent exercise in which the balance of W' remaining ([Formula: see text]) may be calculated with greater accuracy. Field trials of athletes performing stochastic exercise indicate that this [Formula: see text] model can accurately predict the time at which W' tends to zero and exhaustion is imminent. The [Formula: see text] model potentially has important applications in the real-time monitoring of athlete fatigue progression in endurance and team sports, which may inform tactics and influence pacing strategy.

Reliability and validity of a modified 3-minute all-out swimming test in elite swimmers

Critical speed (CS) testing is useful in monitoring training in swimmers, however, completing a series of time trials (TTs) regularly is time-consuming. The 3-minute test may be a solution with positive initial findings. This investigation examined whether a modified 3-minute test (12 × 25 m) could assess CS and supra-CS distance capacity (D') in swimmers. A series of 12 × 25 m intervals were completed unpaced at maximal effort, interspersed with 5 s rest periods. The model speed = a e^bt + c was fitted to the data and integrated to calculate D'. The slowest two of the last four efforts were averaged to calculate CS. To assess reliability, 15 highly trained swimmers (9 females, 6 males) completed the 12 × 25 m twice within 72 h. Four measures were deemed reliable: peak velocity (0.01 m s^-1; 0.5%, typical error and % coefficient of variation), CS (0.02 m s^-1; 1.2%), D' (1.22 m; 5.7%) and drop off % (0.70% points; 4.5%). To assess criterion validity, 21 swimmers (9 from reliability, 12 other) completed two competition races within 2 weeks of a 12 × 25 m in the same stroke. Traditional CS and D' measures were calculated from competition performances (TT method). TT CS and 12 × 25 m CS were highly correlated (adj. R² = 0.92, p < .001). D' values were moderately correlated (adj. R² = 0.60, p < .01). Two TTs may have been too few to estimate D' accurately. The 12 × 25 m all-out swimming test is a reliable method for assessing CS and D' in swimmers, however, the validity of D' requires further investigation.

Considerations on the Assessment and Use of Cycling Performance Metrics and their Integration in the Athlete's Biological Passport

Over the past few decades the possibility to capture real-time data from road cyclists has drastically improved. Given the increasing pressure for improved transparency and openness, there has been an increase in publication of cyclists' physiological and performance data. Recently, it has been suggested that the use of such performance biometrics may be used to strengthen the sensitivity and applicability of the Athlete Biological Passport (ABP) and aid in the fight against doping. This is an interesting concept which has merit, although there are several important factors that need to be considered. These factors include accuracy of the data collected and validity (and reliability) of the subsequent performance modeling. In order to guarantee high quality standards, the implementation of well-structured Quality-Systems within sporting organizations should be considered, and external certifications may be required. Various modeling techniques have been developed, many of which are based on fundamental intensity/time relationships. These models have increased our understanding of performance but are currently limited in their application, for example due to the largely unaccounted effects of environmental factors such as, heat and altitude. In conclusion, in order to use power data as a performance biometric to be integrated in the biological passport, a number of actions must be taken to ensure accuracy of the data and better understand road cycling performance in the field. This article aims to outline considerations in the quantification of cycling performance, also presenting an alternative method (i.e., monitoring race results) to allow for determination of unusual performance improvements.

W' expenditure and reconstitution during severe intensity constant power exercise: mechanistic insight into the determinants of W'

The sustainable duration of severe intensity exercise is well-predicted by critical power (CP) and the curvature constant (W'). The development of the W'BAL model allows for the pattern of W' expenditure and reconstitution to be characterized and this model has been applied to intermittent exercise protocols. The purpose of this investigation was to assess the influence of relaxation phase duration and exercise intensity on W' reconstitution during dynamic constant power severe intensity exercise. Six men (24.6 ± 0.9 years, height: 173.5 ± 1.9 cm, body mass: 78.9 ± 5.6 kg) performed severe intensity dynamic handgrip exercise to task failure using 50% and 20% duty cycles. The W'BAL model was fit to each exercise test and the time constant for W' reconstitution (τW') was determined. The τW' was significantly longer for the 50% duty cycle (1640 ± 262 sec) than the 20% duty cycle (863 ± 84 sec, P = 0.02). Additionally, the relationship between τW' and CP was well described as an exponential decay (r(2) = 0.90, P < 0.0001). In conclusion, the W'BAL model is able to characterize the expenditure and reconstitution of W' across the contraction-relaxation cycles comprising severe intensity constant power handgrip exercise. Moreover, the reconstitution of W' during constant power severe intensity exercise is influenced by the relative exercise intensity, the duration of relaxation between contractions, and CP.

Prediction of Critical Power and W' in Hypoxia: Application to Work-Balance Modelling

Purpose: Develop a prediction equation for critical power (CP) and work above CP (W′) in hypoxia for use in the work-balance (WBAL′) model.

Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W′ at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W′ at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W′ were used to compute W′ during HIIT using differential (WBALdiff′) and integral (WBALint′) forms of the WBAL′ model.

Results: CP decreased at altitude (P < 0.001) as described by 3rd order polynomial function (R² = 0.99). W′ decreased at 4,250 m only (P < 0.001). A double-linear function characterized the effect of altitude on W′ (R² = 0.99). There was no significant effect of parameter input (actual vs. predicted CP and W′) on modelled WBAL′ at 2,250 m (P = 0.24). WBALdiff′ returned higher values than WBALint′ throughout HIIT (P < 0.001). During HIIT, WBALdiff′ was not different to 0 kJ at completion, at 250 m (0.7 ± 2.0 kJ; P = 0.33) and 2,250 m (−1.3 ± 3.5 kJ; P = 0.30). However, WBALint′ was lower than 0 kJ at 250 m (−0.9 ± 1.3 kJ; P = 0.058) and 2,250 m (−2.8 ± 2.8 kJ; P = 0.02).

Conclusion: The altitude prediction equations for CP and W′ developed in this study are suitable for use with the WBAL′ model in acute hypoxia. This enables the application of WBAL′ modelling to training prescription and competition analysis at altitude.

Modeling Intermittent Cycling Performance in Hypoxia Using the Critical Power Concept

PURPOSE

This study investigated the efficacy of an intermittent critical power (CP) model, termed the "work-balance" (W'BAL) model, during high-intensity exercise in hypoxia (HYPO).

METHODS

Eleven trained male cyclists (mean ± SD age, 27 ± 6.6 yr; V˙O2peak, 4.79 ± 0.56 L·min(-1)) completed a maximal ramp test and a 3-min "all-out" test to determine CP and work performed above CP (W'). On another day, an intermittent exercise test to task failure was performed. All procedures were performed in normoxia (NORM) and HYPO (FiO2 ≈ 0.155) in a single-blind, randomized, and counter-balanced experimental design. The W'BAL model was used to calculate the minimum W' (W'BALmin) achieved during the intermittent test. The W'BALmin in HYPO was also calculated using CP + W' derived in NORM (N + H).

RESULTS

In HYPO, there was an 18% decrease in V˙O2peak (4.79 ± 0.56 vs 3.93 ± 0.47 L·min(-1); P < 0.001) and a 9% decrease in CP (347 ± 45 vs 316 ± 46 W; P < 0.001). No significant change for W' occurred (13.4 ± 3.9 vs 13.7 ± 4.9 kJ; P = 0.69; NORM vs HYPO). The change in V˙O2peak was significantly correlated with the change in CP (r = 0.72; P = 0.01). There was no difference between NORM and HYPO for W'BALmin (1.1 ± 0.9 kJ vs 1.2 ± 0.6 kJ). The N + H analysis grossly overestimated W'BALmin (7.8 ± 3.4 kJ) compared with HYPO (P < 0.001).

CONCLUSION

The W'BAL model produced similar results in HYPO and NORM, but only when model parameters were determined under the same environmental conditions as the performance task. Application of the W'BAL model at altitude requires a modification of the model or that CP and W' are measured at altitude.

Power-duration relationship: Physiology, fatigue, and the limits of human performance

The duration that exercise can be maintained decreases as the power requirements increase. In this review, we describe the power-duration (PD) relationship across the full range of attainable power outputs in humans. We show that a remarkably small range of power outputs is sustainable (power outputs below the critical power, CP). We also show that the origin of neuromuscular fatigue differs considerably depending on the exercise intensity domain in which exercise is performed. In the moderate domain (below the lactate threshold, LT), fatigue develops slowly and is predominantly of central origin (residing in the central nervous system). In the heavy domain (above LT but below CP), both central and peripheral (muscle) fatigue are observed. In this domain, fatigue is frequently correlated with the depletion of muscle glycogen. Severe-intensity exercise (above the CP) is associated with progressive derangements of muscle metabolic homeostasis and consequent peripheral fatigue. To counter these effects, muscle activity increases progressively, as does pulmonary oxygen uptake ([Formula: see text]), with task failure being associated with the attainment of [Formula: see text] max. Although the loss of homeostasis and thus fatigue develop more rapidly the higher the power output is above CP, the metabolic disturbance and the degree of peripheral fatigue reach similar values at task failure. We provide evidence that the failure to continue severe-intensity exercise is a physiological phenomenon involving multiple interacting mechanisms which indicate a mismatch between neuromuscular power demand and instantaneous power supply. Valid integrative models of fatigue must account for the PD relationship and its physiological basis.

Critical Power: An Important Fatigue Threshold in Exercise Physiology

: The hyperbolic form of the power-duration relationship is rigorous and highly conserved across species, forms of exercise, and individual muscles/muscle groups. For modalities such as cycling, the relationship resolves to two parameters, the asymptote for power (critical power [CP]) and the so-called W' (work doable above CP), which together predict the tolerable duration of exercise above CP. Crucially, the CP concept integrates sentinel physiological profiles-respiratory, metabolic, and contractile-within a coherent framework that has great scientific and practical utility. Rather than calibrating equivalent exercise intensities relative to metabolically distant parameters such as the lactate threshold or V˙O2max, setting the exercise intensity relative to CP unifies the profile of systemic and intramuscular responses and, if greater than CP, predicts the tolerable duration of exercise until W' is expended, V˙O2max is attained, and intolerance is manifested. CP may be regarded as a "fatigue threshold" in the sense that it separates exercise intensity domains within which the physiological responses to exercise can (CP) be stabilized. The CP concept therefore enables important insights into 1) the principal loci of fatigue development (central vs. peripheral) at different intensities of exercise and 2) mechanisms of cardiovascular and metabolic control and their modulation by factors such as O2 delivery. Practically, the CP concept has great potential application in optimizing athletic training programs and performance as well as improving the life quality for individuals enduring chronic disease.