On the kinetics of anaerobic power
© Moxnes et al.; licensee BioMed Central Ltd. 2012
Received: 13 March 2012
Accepted: 20 June 2012
Published: 25 July 2012
This study investigated two different mathematical models for the kinetics of anaerobic power. Model 1 assumes that the work power is linear with the work rate, while Model 2 assumes a linear relationship between the alactic anaerobic power and the rate of change of the aerobic power. In order to test these models, a cross country skier ran with poles on a treadmill at different exercise intensities. The aerobic power, based on the measured oxygen uptake, was used as input to the models, whereas the simulated blood lactate concentration was compared with experimental results. Thereafter, the metabolic rate from phosphocreatine break down was calculated theoretically. Finally, the models were used to compare phosphocreatine break down during continuous and interval exercises.
Good similarity was found between experimental and simulated blood lactate concentration during steady state exercise intensities. The measured blood lactate concentrations were lower than simulated for intensities above the lactate threshold, but higher than simulated during recovery after high intensity exercise when the simulated lactate concentration was averaged over the whole lactate space. This fit was improved when the simulated lactate concentration was separated into two compartments; muscles + internal organs and blood. Model 2 gave a better behavior of alactic energy than Model 1 when compared against invasive measurements presented in the literature. During continuous exercise, Model 2 showed that the alactic energy storage decreased with time, whereas Model 1 showed a minimum value when steady state aerobic conditions were achieved. During interval exercise the two models showed similar patterns of alactic energy.
The current study provides useful insight on the kinetics of anaerobic power. Overall, our data indicate that blood lactate levels can be accurately modeled during steady state, and suggests a linear relationship between the alactic anaerobic power and the rate of change of the aerobic power.
During human exercise, it is well known that intracellular adenosine triphosphate (ATP) can be produced aerobically in the mitochondria by oxidative phosphorylation, anaerobically due to glycolysis or glycogenolysis, or by the breakdown of phosphocreatine (PCr) into Creatine (Cr) in the Creatine Kinease (CK) reaction. With the aerobic energy as input, the current paper develops mathematical models that simulate the kinetics of anaerobic ATP production and power.
The rate of oxygen (O2) consumption can be set to the sum of a constant rate (resting rate of O2 consumption), a rate due to unloaded body movements and at a rate that is used to perform work . For moderate constant work rates, the O2 consumption increases to a steady state condition. However, at the onset of exercise or due to a change in work rate, there is a rate of change of VO2 (or aerobic power) that is named as the VO2 kinetics. Pulmonary rate of O2 consumption (VO2p) has been used as a proxy for VO2. For moderate intensity exercise at constant work rate below the lactate threshold three distinct phases have been observed for VO2p. Phase I is the cardio dynamic phase, which represents the circulatory transit delay from muscles to lungs. Phase II is the mono exponential increase of VO2p. This phase reflects the adjustment of VO2 due to the use of active skeletal muscles. Phase III is the steady state phase of VO2p and VO2 during moderate exercise intensities [2, 3]. For work rates associated with sustained acidosis, the mono-exponential component is slowed compared with lower intensities below the lactate threshold. In addition, a delayed slow component is superimposed. The slow component begins around 100-200 s into the exercise and can result in either a delayed sub-maximal steady state or a steady state equal to the maximal oxygen uptake (VO2 max) . However, the mechanism of this slow component has not been resolved . In this paper, we model phase II and III because only these two phases are considered relevant for the anaerobic alactic power model.
When exercise intensity increases and the rate of ATP production by oxidative sources becomes insufficient, anaerobic ATP production is required. When ATP is produced by glycolysis or glycogenolysis the endpoint is pyruvate, which can be reduced to lactate or oxidized to CO2 or H2O. The blood lactate concentration in the lactate pool is the result of the appearance of lactate from working muscles and various tissues and the disappearance of lactate in the skeletal muscles, the heart, the liver and the kidney cortex [6–9]. During steady state, lactate production (influx) is equal to lactate removal (outflux). As a result, the lactate concentration in the lactate pool stays constant, and the rate of oxygen consumption is the measure of the whole body energy expenditure regardless of the magnitude of lactate production and removal or the absolute blood lactate concentration . The concept of a maximal lactate steady state can be defined as the highest level of intensity where a steady state condition of lactate can be obtained, which is also referred to as the lactate threshold. At exercise intensities above the lactate threshold the rise in the lactate concentration could be attributed to an increase in the rate of lactate appearance or the result of a decrease in the rate of lactate disappearance .
The maximum anaerobic energy that can be utilized is proportional to the sum of Cr and lactate that can accumulate in the body. PCr is an energy buffer that supports the transient failure of other metabolic pathways to support ATP production. The equilibrium constant of the CK reaction is around 20 and the slightest drop in ATP allows the reaction to proceed to ATP production . Thus, the ATP concentration stays nearly constant until almost all the PCr is utilized. Rossiter et al.  found that the PCr levels follow an exponential time course after changes in work rate before approaching a steady state condition at moderate exercise intensities. In such cases, a strong similarity has been reported for the time constants of the VO2 kinetics and the PCr consumption . Margaria  was the first to propose a hydraulic model for examining the whole body energy process during exercise. Despite this breakthrough, Margaria’s model was not quantified. Morton  presented a generalization of this model that was solved mathematically and compared with experimental data. Here Morton  modeled aerobic power and alactic anaerobic power with an exponential time devolvement without any anaerobic lactic power during steady state exercises below the lactate threshold. However, for exercise intensities that are above the lactate threshold, the anaerobic glycolytic energy supply is significant. The association between PCr and VO2 rate constants for exercises at such intensities has not yet been systematically reported. Furthermore, during recovery after high intensity exercise, the level of PCr must be restored, the pH must be re-established and ADP removed. While the PCr recovery is mainly due to oxidative ATP synthesis, the PCr stores may be rebuilt by anaerobic glycolysis [15–17]. Altogether, these insights provide a theoretical background for the models developed in this paper.
The O2-deficit formula of Medbø et al.  is an alternative model for the anaerobic power that accounts for lactic and alactic anaerobic power in which the chemical coupling efficiencies are assumed to be similar. Medbø et al.  suggested that the O2-deficit can be calculated by assuming that the metabolic power at intensities above VO2max can be estimated by extrapolating the steady state linear relationship between work rate and VO2 at submaximal intensities. The validity of the O2-deficit method has been widely debated [e.g., [19–21]. However, a rationale for the O2-deficit model is the assumption that the chemical coupling efficiencies of the three sources of ATP synthesis are similar.
The current study investigated two different mathematical models for the kinetics of anaerobic power during whole body exercise at different intensities. In order to test the models during exercise, oxygen uptake and blood lactate concentration were measured while a cross country skier ran with poles on a treadmill. Aerobic power, based on oxygen uptake measurements, was used as input. The lactic anaerobic power was calculated with a model presented by Moxnes and Hausken  using lactate concentration averaged over the whole lactate space, and with a model of Moxnes and Sandbakk  where the lactate concentration was separated into two compartments: muscles + internal organs and blood. The current study has input from these two previous studies, and focuses on alactic power as a novel contribution. Therefore, the power due to PCr break down was calculated theoretically and compared against the results of Jeneson et al. . Finally, the models were used to compare PCr break down during continuous and interval exercise.
The current study simulated the kinetics of anaerobic powers during whole body exercise at different exercise intensities through the use of Mathematica 8 (Wolfram Research Inc., Champaign, IL, USA). In a first model (Model 1), we suggested that for all exercise intensities, the work power would be linear to the work rate. As an alternative model (Model 2) where the lactic power and energy were the same as for Model 1, we hypothesized that the alactic anaerobic power would be proportional to the rate of change of the aerobic power [24, 25]. For both models we used a first order differential equation for the time development of aerobic power as a function of work rate. As most parameters in the models needed to be fitted to each individual athlete, we fitted all parameters to a male Norwegian national level cross-country skier, with a body mass of m = 77.5 kg, body height of 181 cm and 600 h of endurance training per year. Thereafter, the simulations were compared with experimental data where this skier was running with poles on a treadmill (see details below).
The aerobic and anaerobic powers
In these equations, “model” means model assumption. Q r is the resting metabolic power, set to 80 J/s based on oxygen uptake measurements of this skier during rest in our laboratory. is ATP production by glycolysis/glycogenolysis and CK by phosphocreatine break down to creatine. is aerobic power due to internal and external work. The aerobic power is .
where “def” means definition. η a , η G and η CK are the chemical coupling efficiencies when producing ATP aerobically, anaerobically by glycolysis/glycogenolysis (G), and anaerobically by CK, respectively. The two chemical coupling efficiencies η G and η a are similar, whereas η CK is larger . η is the mechanical efficiency, which means the work per unit use of energy of 1 ATP. P T includes both internal and external work.
where c is a parameter. P is the work rate. Q un is the metabolic power due to unloaded running with poles. In general, Q un depends on the angle of inclination and the cycle frequency of the activity. For a given incline the frequency depends of the work rate. Thus Q un becomes a function of the work rate even for a fixed incline. includes both internal and external work power. is input to the further modeling. We set that is a linear interpolation function through our data points. For values above or below the maximum and minimum measured we used linear extrapolation. During steady state, .
The “dot” above a variable means time derivative. τ a is the so-called e-folding time, which is the time interval when an exponentially growing quantity increases by a factor of e. Thus τ a is a time parameter that scales from the initiation of the activity until the aerobic power reaches a steady state asymptotically when assuming a constant work rate. In practice, reaching steady state means having less than 1% change in aerobic power per second. The cardio dynamic phase is not necessary to account for since only the true aerobic power is used as input for our anaerobic models. Typical values for τ a are shown to be 10-36 s for moderate intensity exercise [22, 27–33]. For example, di Prampero  suggested 10-24 s, Ceretelli et al.  found that τ a increases linearly with the concentration of lactate up to 36 s, and Bizoni et al.  found τ a = 23 s for all work rates. We set that τ a = 30 s as a compromise. Equation (4b) ensures that the aerobic power is less than the maximal aerobic power.
If the work rate is sufficiently low, . Notable is that solution (6) will only apply for a restricted time period unless .
C is the lactate concentration defined as the amount of lactate per unit volume of lactate space (including muscles and blood). m is the mass of the body. λ converts the lactate per unit body mass to oxygen consumption. Note that during dynamic situations the blood lactate concentration differs significantly from the muscle lactate concentration. In this article, we measured the blood lactate concentration, which is different from the muscle lactate concentration. However, earlier research has demonstrated that the lactate concentration can be calculated as summarized in Appendix A .
Experimentally, E G (t0, t) can be found by measuring changes in the lactate concentration before and after exercise.
Steady state is achieved at exercise intensities below the lactate threshold. We defined steady state with the lactate concentration regarded as steady (i.e., no lactic power) and the aerobic power was steady, to read . Thus is a constant and during steady state. Hence, it follows that Q CK = 0. Thus the alactic power is zero. To achieve Q CK = 0 during steady state in (9) we must have . However, during steady state (below LT) . Thus, below the lactate threshold during steady state.
To calculate for a work rate P(t) in (12a) we inserted from (3), Q a (t) from (4) and Q G (t) from (7). It should be noted that the mechanical efficiency η is absent in (12a and b). We conceived that the work power P T was proportional to the rate of consumption of ATP. Thus, an equivalent model to (11) would be that the rate of ATP consumption is linear with the work rate.
where E DF (t0, t) is in the literature named the oxygen deficit of the exercise. Medbø et al.  and Losnegard et al.  calculated the anaerobic energy during exercise as E DF (t0, t) (assuming that the rate of change of O2 consumption is proportional to the aerobic power). Equation (14b) shows that only if . Thus, we deduced that the O2-deficit model for the anaerobic energy was equal to (14b) if the chemical coupling efficiencies were alike.
The alactic energy can be found by calculating the area between and Q a in a power time diagram. This area is multiplied with θ/τ a to find the alactic energy used. θ/τ a can be considered as the effectiveness of alactic ATP production relative to the aerobic ATP production. Thus we set .
The mechanical efficiency was assumed to be η ≈ 0.5. For the chemical efficiency related to aerobic or lactic power . We used . This gave . Since c was around 6.6 we found that . Gonzales-Alonso et al.  concluded from experimental data that the heat per use of ATP was around two times larger for oxidative phosphorylation and anaerobic glycolysis compared to ATP from CK. For the alactic power we therefore forecasted that η CK = 0.95. This gave that .
It has been shown that during intensities above the lactate threshold, ATP utilization increases and mechanical efficiency decreases at constant work rates. This may be explained by a change in fiber type recruitment, an elevated temperature, lowered pH or increased Pi levels . We forecasted that τ a , η, η a , η G and η CK mainly depend on the lactate concentration and change in muscle pH, and assumed that a lower mechanical efficiency of muscle contractions and a lower P/O2 ratio could explain the drift in the steady state ATP consumption for a given work rate. To account for time varying parameters see the model in Appendix B.
We also defined the so-called excess post–exercise oxygen consumption (EPOC). In general E DB (t1, t2) is different from EPOC(t1, t2) unless . However, the latter is usually the case during recovery.
Here, the alactic energy used from time t0 to time t1, could be found by measuring the aerobic power at time t1 and t2. The respective aerobic powers could be subtracted and then multiplied with θ to find the alactic energy.
These two methods gave different results due to fact that the second term for lacks in Model 2.
If η G = η a , EPOC(t1, t2) corresponds to the total anaerobic energy, i.e. lactic and alactic anaerobic energy. Indeed, Margaria et al.  later on modified the concept of Hill et al. [34–37], and suggested that the increased aerobic power consisted of the fast alactic component and the slower lactic component. Finally, Gaesser and Brooks  introduced the term “excess post-exercise oxygen consumption”, which also included the much more prolonged increase in aerobic power that is observed for hours after exercise. Model 1 did not account for this very slow component that lasts for hours. Model 2 in equation (25b) said that EPOC corresponded to the alactic component. However, the development of the energy in equation (25b) depended of the time dynamics for the aerobic power modeled according to equation (4).
The derived mathematical simulations were compared with experimental data from an elite cross-country skier while running with poles on a treadmill. The mass of the skier was m = 77.5 kg in all tests. All treadmill tests were performed on a 6 × 3 m motor-driven treadmill (Bonte Technology, Zwolle, The Netherlands). Inclination and speed were calibrated using the Qualisys Pro Reflex system and the Qualisys Track Manager software (Qualisys AB, Gothenburg, Sweden). The treadmill belt consisted of a non-slip rubber surface that allowed the skier to use his own poles (pole length: 80% of body height) with special carbide tips. Gas exchange values were measured by open-circuit indirect calorimetry using an Oxycon Pro apparatus with a mixing chamber (Jaeger GmbH, Hoechberg, Germany). Before each measurement, the VO2 and VCO2 gas analyzers were calibrated using high-precision gases (16.00 ± 0.04% O2 and 5.00 ± 0.1% CO2, Riessner-Gase GmbH & co, Lichtenfels, Germany). The inspiratory flow meter was calibrated with a 3 L volume syringe (Hans Rudolph Inc., Kansas City, MO). Heart rate (HR) was measured with a heart rate monitor (Polar S610, Polar Electro OY, Kempele, Finland), using a 5 s interval for data storage. Blood lactate concentration (BLa) was measured on 5 μL samples taken from the fingertip by a Lactate Pro LT-1710 t (ArkRay Inc, Kyoto, Japan).
Eight experimental protocols were performed, each on separate days with a minimum of 48 h between. The order of tests was performed as presented below. In order to investigate whole body exercise, running with poles was used in all tests. Before each testing session a standardized, test-specific 20-min warm-up was performed. Training on the days before testing was standardized, and the subject drank a standard fluid with sugar and electrolytes during all breaks while testing.
On the first test day, the skier performed six 5 min bouts with constant work rates at 0.105 inclines in radians. Five speeds at 0.25 m/s intervals below the lactate threshold were chosen, starting at 7.5 km/h = 2.08 m/s, followed by 2.33 m/s, 2.58 m/s, 2.83 m/s and 3.08 m/s. The sixth speed was increased by 0.125 m/s, giving 3.19 m/s, which is slightly above the lactate threshold. The starting speed was chosen based on experience from earlier tests of this athlete. 5-min bouts were used to obtain steady state conditions. A 2-min break with low-intensity walking was mandatory between each of the exercise bouts. Gas exchange values and heart rates were determined by the average of the last minute during each stage and blood lactate was measured directly after finishing each stage. The reason for using the last minute to assess respiratory variables was that the athletes are able to keep a more steady technique and physiological responses after 3-4 min.
2.87 m/s was performed for 800 s.
3.08 m/s was performed over 800 s, followed by a 5-min recovery phase at 1.67 m/s with blood lactate measured after 2 and 5 min. Additionally, a 3.05 m/s stage was performed to exhaustion.
3.08 m/s was performed over 2000 s.
3.19 m/s stages were performed for 400 s and to exhaustion.
A 3.33 m/s stage was performed for 400 s.
3.88 m/s stages were performed for 150 s, 200 s and to exhaustion.
Finally, maximal metabolic power was tested on a separate day at an incline of 0.105 radians with a starting speed of 3 m/s. The speed was increased by 0.3 m/s every minute until exhaustion. VO2 was measured continuously, and the average of the three highest 10 s consecutive measurements determined VO2max and was used to calculate the maximal metabolic power. The test was considered to be a maximal effort if the following three criteria were met: 1) a plateau in VO2 was obtained with increasing exercise intensity, 2) respiratory exchange ratio above 1.10, and 3) blood lactate concentration exceeding 8 mmol/L.
In the first step, we applied visual curve fitting, which means that we chose plausible values for the two parameters. We then plotted and compared the solution visually with the experimental data. The values of the two parameters were changed repeatedly until a good visual fit was obtained, while ensuring that the parameters had physiologically trustworthy numerical values. In the second step, a least square fit to the data was performed to produce ex post best fit estimates of these two parameters using the visual estimates as starting guess points and choosing a range around each starting point of the parameters. The method was performed separately for each parameter keeping the other parameter fixed. Steps 1 and 2 were repeated sufficiently until we were certain that we had obtained the optimal values for each of the two parameters. For robustness in the calculations, we applied the steady state solution. If we applied that , the parameter χ got removed. The lactate threshold became Q a = Q max . χ determined the lactate threshold by the exact relation. The rest of the lactate curve was dependent on . This gave a linear dependency of the lactate curve on p0/d0 and an inverse dependency of the lactate curve on Q max − Q a .
The current study investigated two different mathematical models for the kinetics of anaerobic power during whole body exercise at different exercise intensities. The results indicate that blood lactate levels can be accurately modeled during steady state, and suggest a linear relationship between the alactic anaerobic power and the rate of change of the aerobic power. Overall, we propose that the current simulation models provide useful insight into how the anaerobic powers during practical training and testing should be interpreted.
When comparing the experimental blood lactate values with Moxnes and Hausken’s  model for lactic power, we found similar results during steady state exercise intensities. Uncertainties of less than ± 0.5 mmol/L using these mathematical models are indicated. For intensities above the lactate threshold, the measured blood lactate values were significantly lower than those calculated by the Moxnes and Hausken  model, whereas during recovery after high intensity exercise, the blood lactate concentration was higher than calculated. This could be explained by a possible delay in blood lactate concentration compared to the average concentration in the total lactate pool that applies theoretically. Therefore, we compared the experimental results with a two-compartment model by Moxnes and Sandbakk , where lactate concentration was separated into muscle and blood compartments. The employment of this model gave a better fit between simulated and experimental results, especially for the highest work rates and during recovery. However, some discrepancies still appeared for the higher work rates, which is a topic for further research.
The calculation of alactic storage of energy in Model 1, which is a generalization of the O2-deficit model, showed that the alactic energy storage increased due to use of lactic power from the time the aerobic power reached a steady state. However, the various inferences about the alactic storage of energy during steady state work rate have some shortcomings. For example, the results may have been influenced by the fact that the O2-deficit is composed of both lactic and alactic metabolisms. Thus, no definite conclusion could be drawn for Model 1 for alactic power. For Model 2, the alactic energy showed a simpler behavior since it only depended on the rate of change of the aerobic power. When comparing Models 1 and 2 with the invasive measurements by Jeneson et al. , we concluded that Model 2 gave the best behavior. Thus, a linear relationship between the alactic anaerobic power and the rate of change of the aerobic power was suggested. In future research, it remains a challenge to confirm the contribution of lactic and alactic anaerobic metabolism and power.
The simulations of interval exercises showed a drop in lactate levels between the intervals, and that the alactic energy stores were rebuilt during recovery. Practically, this allows athletes to work at high exercise intensities for longer time durations without lowering the pH values in the muscles compared to continuous exercise at similar intensities. This might be one reason for the effectiveness of interval exercise, as verified both by the scientific literature  and by elite athletes’ practical training programs where interval exercises are an important component. When the alactic anaerobic powers during continuous exercises from Models 1 and 2 were compared, Model 1 showed a local minimum appearing around the time when the aerobic power reached steady state for continuous exercise, whereas for Model 2 the alactic energy storage decreased with time. During interval exercise, the alactic energy storage as a function of time was similar for the two models.
Modeling in human biology is a challenge since one is confronted with conceiving a simple, but realistic representation of complex phenomena. The parameter values used in the current study will be dependent on the fitness level of the individual being tested and on the concentration of glycogen in the muscles. To construct valid simulation models during dynamic exercise such models need to be developed for each individual.
Appendix A: The lactate model
Appendix B: Model for parameters varying with the lactate level
- Cabrera ME, Saidel GM, Kalhan SC: Lactate metabolism during exercise: analysis by an integrative systems model. Am J Physiol. , 277: R1522-R1536.PubMedGoogle Scholar
- Stirling JR, Zakynthinaki MS, Saltin B: A model of oxygen uptake kinetics in response to exercise: Including a means of calculating oxygen demand/deficit/debt. Bull Math Biol. 2005, 67: 989-1015. 10.1016/j.bulm.2004.12.005.View ArticlePubMedGoogle Scholar
- Murias JM, Spencer MD, Kowalchuk JM, Paterson DH: Influence of phase I duration on phase II VO2 kinetics parameters estimates in older and young adults. Am J Physiol Regul Integr Comp Physiol. 2011, 301: R218-R224. 10.1152/ajpregu.00060.2011.View ArticlePubMedGoogle Scholar
- Billat VL, Bocquet V, Slawinski J, Laffitte L, Demarle A, Chassaing P, Koralsztein JP: Effects of prior intermitted runs av v VOmax on oxygen kinetics during an all-out severe run in humans. J Sports Med Phys Fitness. 2000, 40: 185-194.PubMedGoogle Scholar
- Gaesser GA, Poole DC: The slow component of oxygen uptake in humans. Exerc Sport Sci Rev. 1996, 24: 35-70.View ArticlePubMedGoogle Scholar
- Brooks GA: Lactate production under fully aerobic conditions: the lactate shuttle during rest and exercise. Fed Proc. 1985, 45: 2924-2929.Google Scholar
- Brooks GA: Current concepts in lactate exchange. Med Sci Sports Exerc. 1991, 23: 895-906.View ArticlePubMedGoogle Scholar
- Brooks GA: Lactate shuttles in nature. Biochem Soc Trans. 2002, 30: 258-264.View ArticlePubMedGoogle Scholar
- Brooks GA: Link between glycolytic and oxidative metabolism. Sports Med. 2007, 37: 341-343. 10.2165/00007256-200737040-00017.View ArticlePubMedGoogle Scholar
- Moxnes JF, Hausken K: A mathematical model for the training impulse and lactate influx and outflux during exercise. Int J Mod Phys C. 2009, 20 (1): 147-177. 10.1142/S0129183109013522.View ArticleGoogle Scholar
- Carlson FD, Siger A: The mechano-chemistry of muscular contraction I. The isimetric twitch. J Gen Physiol. 1960, 44: 33-60. 10.1085/jgp.44.1.33.PubMed CentralView ArticlePubMedGoogle Scholar
- Rossiter HB, Ward SA, Doyle VL, Howe FA, Griffiths JR, Whipp BJ: Interference from pulmonary O2 uptake with respect to intramuscular (phosphcreatine) kinetcs during moderate exercise in humans. J Physiol. 1999, 518: 921-932. 10.1111/j.1469-7793.1999.0921p.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Margaria R: Biomechanics and energetic of muscular exercise. 1976, Oxford: Oxford University PressGoogle Scholar
- Morton RH: A three component model of human bioenergetics. J Math Biol. 1986, 24: 451-466. 10.1007/BF01236892.View ArticlePubMedGoogle Scholar
- Crowther GJ, Kemper WF, Carey MF, Conley KE: Control of glycolysis in contracting skeletal muscle. II. Turning it off. Am J Physiol Endocrinol Metab. 2002, 282: 74-79.Google Scholar
- Jubrias SA, Esselman PC, Price LB, Cress ME, Conley KE: Large energetic adaptations of elderly muscle to resistance and endurance training. J Appl Physiol. 2001, 90: 1663-1670. 10.1063/1.1374452.View ArticlePubMedGoogle Scholar
- Lanza IR, Wigmore DM, Befroy DE, Kent-Braun JA: In vivo ATP production during free-flow and ischaemic muscle contractions in humans. J Physiol. 2006, 577: 353-367. 10.1113/jphysiol.2006.114249.PubMed CentralView ArticlePubMedGoogle Scholar
- Medbø JI, Mohn AC, Tabata I, Bahr R, Vaage O, Sejersted OM: Anaerobic capacity determined by maximum accumulated O2 deficit. J Appl Physiol. 1988, 64: 50-60.PubMedGoogle Scholar
- Bangsbo J: Quantification of anaerobic energy production during intense exercise. Med Sci Sports Exerc. 1998, 30: 47-52.View ArticlePubMedGoogle Scholar
- Gastin PB: Energy system interaction and relative contribution during maximal exercise. Sports Med. 2001, 31: 725-741. 10.2165/00007256-200131100-00003.View ArticlePubMedGoogle Scholar
- Noordhof DA, de Kroning JJ, Foster C: The maximal accumulated oxygen deficit method: a valid and reliable measure of anaerobic capacity?. Sports Med. 2010, 40: 285-302. 10.2165/11530390-000000000-00000.View ArticlePubMedGoogle Scholar
- Moxnes JF, Sandbakk K: The kinetics of lactate production and removal during whole-body exercise. Theor Biol Med Model. 2012, 9: 7-10.1186/1742-4682-9-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Jeneson JAL, Schmitz JPJ, van den Broek NMA, van Riel AAW, Hilbers PAJ, Nicolay K, Prompers JJ: Magnitude and control of mitocondrial sensitivity to ADP. Am J Physiol Endocrinol Metabol. 2009, 297: E774-E784. 10.1152/ajpendo.00370.2009.View ArticleGoogle Scholar
- Mahler M: First order kinetics of muscle oxygen consumption, and an equivalent proportionality between Qo2 and Phosphorylcreatine level. J Gen Physiol. 1985, 86: 135-165. 10.1085/jgp.86.1.135.View ArticlePubMedGoogle Scholar
- Meyer RA: A linear model of muscle respiration explains monoexponential phosphocreatine changes. J Appl Physiol. 1988, 75: 648-656.Google Scholar
- Gonzalez-Alonso J, Quistorff B, Krustrup P, Bangsbo J, Saltin B: Heat production in human skeletal muscle at the onset of intense dynamic exercise. J Physiol. 2000, 524 (2): 603-615. 10.1111/j.1469-7793.2000.00603.x.PubMed CentralView ArticlePubMedGoogle Scholar
- di Prampero PE, Ferretti G: The energetics of anaerobic muscle metabolism: a reappraisal of older and recent concepts. Respir Physiol. 1999, 118 (2–3): 103-115.View ArticlePubMedGoogle Scholar
- di Prampero PE: Factors limiting maximal performance in humans. Eur J Physiol. 2003, 90: 420-429. 10.1007/s00421-003-0926-z.View ArticleGoogle Scholar
- Losnegard T, Myklebust H, Hallen J: Energy system contribution as a determinant of performance in elite skiers. 2011, 10.1249/MSS.0b013e3182388684.Google Scholar
- Bangsbo J, Krustrup P, Gonzalez-Alonso J, Saltin B: ATP production and efficiency of human skeletal muscle during intense exercise: effects of previous exercise. Am J Physiol Endocrinol Metabol. 2001, 280: E956-E964.Google Scholar
- Bertuzzi RC, Franchini E, Ugrinowitsch C, Kokubun E, Lima-Silva AE, Pires FO, Nakamura FY, Kiss MA: Predicting MAOD using only a supramaximal exhaustive test. Int J Sports Med. 2010, 31 (7): 477-481. 10.1055/s-0030-1253375.View ArticlePubMedGoogle Scholar
- Cerretelli P, Pendergast DR, Paganelli WC, Rennie DW: Effects of specific muscle training on VO2-on response and early blood lactate. J Appl Physiol. 1979, 47: 761-769.PubMedGoogle Scholar
- Binzoni T, Ferretti G, Sechenker K, Cerretelli P: Phosphocreatine hydrolysis in 31P-NMR at the onset of constant-load exercise in humans. J Appl Physiol. 1992, 73: 1644-1649.PubMedGoogle Scholar
- Hill VH, Lupton H: Muscular exercise, lactic acid, and the supply and utilization of oxygen. Q J Med. 1923, 16: 135-171. 10.1093/qjmed/os-16.62.135.View ArticleGoogle Scholar
- Hill AV, Long CNH, Lupton H: Muscular exercise, lactic acid, and the supply and utilization of oxygen: parts I-III. Proc Roy Soc. 1924, 96: 438-475. 10.1098/rspb.1924.0037.View ArticleGoogle Scholar
- Hill AV, Long CNH, Lupton H: Muscular exercise, lactic acid, and the supply and utilization of oxygen: parts IV-VI. Proc Roy Soc. 1924, 97: 84-138. 10.1098/rspb.1924.0045.View ArticleGoogle Scholar
- Hill AV, Long CNH, Lupton H: Muscular exercise, lactic acid, and the supply and utilization of oxygen: parts VII-VIII. Proc Roy Soc. 1924, 97: 155-176. 10.1098/rspb.1924.0048.View ArticleGoogle Scholar
- Margaria R, Edwards HT, Dill OB: The possible mechanisms of contracting and paying the oxygen dept and the role of lactic acid in muscular contraction. Am J Physiol. 1933, 106: 689-715.Google Scholar
- Gaesser GA, Brooks GA: Metabolic bases of excess post-exercise oxygen consumption: a review. Med Sci Sports Exerc. 1984, 16: 29-43.PubMedGoogle Scholar
- Laursen PB, Jenkins DG: The scientific basis for high-intensity interval training: Optimising training programmes and maximizing performance in highly trained endurance athletes. Sports Med. 2002, 32: 53-73. 10.2165/00007256-200232010-00003.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.