Critical Power Model Research Articles

Wearable heart rate sensing and critical power-based whole-body fatigue monitoring in the field

Whole-body fatigue (WBF) presents a concerning risk to construction workers, which can impact function and ultimately lead to accidents and diminished productivity. This study proposes a new WBF monitoring technique by applying the Critical Power (CP) model, a bioenergetic model, with a wrist-worn heart rate sensor. The authors modified the CP model to calculate WBF from the percentage of heart rate reserve (%HRR) and generated a personalized model via WBF perception surveys. Data were collected for two days from 33 workers at four construction sites. The results showed that the proposed technique can monitor field workers’ perceived WBF with a mean absolute error of 12.8% and Spearman correlation coefficient of 0.83. This study, therefore, demonstrates the viability of wearable WBF monitoring on construction sites to support programs aimed at improving workplace safety and productivity.

Parkinson's Critical Heart Rate Test: Applying the Critical Power Model for People with Parkinson's Disease.

Aerobic exercise with the correct intensity can attenuate motor and non-motor symptoms of Parkinson's disease (PD) and improve the quality of life. However, a specific, validated, non-invasive, and outside the laboratory protocol that assesses physiological variables to prescribe optimal aerobic exercise intensity for people with PD is nonexistent. Therefore, this study aimed to propose a protocol, the Parkinson's critical heart rate test (Parkinson-CHR test), to determine the critical heart rate (CHR) in individuals with PD and verify its validity, reliability, and sensitivity. Fifteen people with idiopathic PD, who were able to practice exercises, were recruited to participate in the study (71.1 ± 6.6 years). The study consisted of two experiments: i) the first one aimed to assess the validity and reliability of the protocol, with participants performing the test twice at a one-week interval; ii) the second experiment aimed to investigate the protocol sensitivity, with individuals being evaluated before and after an 8-week training program according to Parkinson-CHR intensity. In experiment 1, no differences between test and retest were observed in the time to cover the distances (400, 800 and 1200 m), the total heart rate, the critical heart rate, and critical speed (p > 0.05). In experiment 2, there was a reduction in time to cover 400 and 800 m as well as in the total heart rate for all distances after the 8-week training program. The Parkinson-CHR test is a reliable, reproducible, inexpensive, and non-invasive protocol to assess, prescribe, and monitor aerobic exercise intensity in people with PD.

Letter to the Editor on: "Running Critical Power: A Comparison Of Different Theoretical Models".

Dear Editor,I read with interest the study by Ruiz-Alias et al. on different critical power models in running 1. The study offers valuable insights into the application of critical power in running, highlighting considerable discrepancies between different methods of estimation. While the work is commendable, it leaves room for questions regarding the methodology applied and the interpretation of the results. The omission of each participant's mean power of the trials in the study, in combination with a reluctance to share it upon request, makes it impossible for the reader to verify the results. At a bare minimum, in studies of this nature, each individual's CP and W', calculated using the different methods, should be made available as it offers valuable information for readers. I elaborate on my main concerns in the following. Accompanying figures are provided in an external repository 2.

Mathematical modeling of exercise fatigability in the severe domain: A unifying integrative framework in isokinetic condition

Muscle fatigue is the decay in the ability of muscles to generate force, and results from neural and metabolic perturbations. This article presents an integrative mathematical model that describes the decrease in maximal force capacity (i.e. fatigue) over exercises performed at intensities above the critical force Fc (i.e. severe domain). The model unifies the previous Critical Power Model and All-Out Model and can be applied to any exercise described by a changing force F over time. The assumptions of the model are (i) isokinetic conditions, an intensity domain of Fc<F, (ii) constant individual parameters of Fc, and τ, (iii) fatigability proportional to the accumulation of impulse JAC above the critical force Fc. The present study adjusted the parameters to fit previous experimental observations from three types of exercises: constant intensity, all-out, and increasing ramp exercise. This integrative model establishes a connection between the previous individual models and provides an excellent description of exercise fatigability in the severe domain. With this new integrative model, the maximal force production of the muscle can be predicted over time during both maximal and submaximal exercises.

Can We Predict Long-Duration Running Power Output? Validity of the Critical Power, Power Law, and Logarithmic Models.

Ruiz-Alias, SA, Ñancupil-Andrade, AA, Pérez-Castilla, A, and García-Pinillos, F. Can we predict long-duration running power output? Validity of the critical power, power law, and logarithmic models. J Strength Cond Res 38(2): 306-310, 2024-Predicting long-distance running performance has always been a challenge for athletes and practitioners. To ease this task, different empirical models have been proposed to model the drop of the running work rate with the increase of time. Therefore, this study aims to determine the validity of different models (i.e., CP, power law, and Peronnet) to predict long-duration running power output (i.e., 30 and 60 minutes). In a 4-week training period, 15 highly trained athletes performed 7-time trials (i.e., 3, 4, 5, 10, 20, 30, and 60 minutes) in a randomized order. Then, their power-duration curves (PDCs) were defined through the work-time critical power model (CP work ), power-1/time (CP 1/time ), 2-parameter hyperbolic (CP 2hyp ), 3-parameter hyperbolic (CP 3hyp ), the undisclosed Stryd (CP stryd ), and Golden Cheetah (CP cheetah ) proprietary models, and the power law and Peronnet models using the 3 to 20 minutes time trials. These ones were extrapolated to the 30- and 60-minute power output and compared with the actual performance. The CP 2hyp , CP 3hyp , CP stryd , and CP cheetah provided valid 30- and 60-minute power output estimations (≤2.6%). The CP work and CP 1/time presented a large predicting error for 30 minutes (≥4.4%), which increased for 60 minutes (≥8.1%). The power law and Peronnet models progressively increased their predicting error at the longest duration (30 minutes: ≤-1.6%; 60 minutes: ≤-6.6%), which was conditioned by the endurance capability of the athletes. Therefore, athletes and practitioners are encouraged to applicate the aforementioned valid models to their PDC to estimate the 30-minute and 60-minute power output.

Running Critical Power: A Comparison Of Different Theoretical Models.

This study aimed (i) to compare the critical power (CP) and work capacity over CP (W´) values reported by the different CP models available in current analysis software packages (Golden Cheetah and Stryd platform), (ii) to locate the CP values in the power-duration curve (PDC), and (iii) to determine the influence of the CP model used on the W´ balance. Fifteen trained athletes performed four time trials (i. e., 3, 5, 10, 20 minutes) to define their PDC through different CP models: work-time (CPwork), power-1/time (CP1/time), Morton hyperbolic (CPhyp), Stryd platform (CPstryd), and Bioenergetic Golden Cheetah (CPCheetah). Three additional time trials were performed: two to locate the CP values in the PDC (30 and 60 minutes), and one to test the validity of the W' balance model (4 minutes). Significant differences (p<0.001) were reported between models for the estimated parameters (CP, W´). CPcheetah was associated with the power output developed between 10 to 20 minutes, CP1/time, CPstryd CPwork and CPhyp. The W´ reported by the three-parameter CP models overestimated the actual 4 minutes time to exhaustion, with CPwork (0.48 [- 0.19 to 1.16] minutes); and CP1/time (0.40 [- 0.13 to 0.94] minutes) being the only valid models (p≥0.240).

Determining critical power and W′ in running: Accuracy of different two-point models using the power metric

This study aims to determine the validity of the critical power (CP) and the work capacity over CP (W′) obtained from different two-time trial combinations with respect a five-point model. In a 3-week training period, 15 athletes (age: 23 ± 5 years; height: 166 ± 6 cm; body mass: 58 ± 8 kg; 5 km season-best: 15:29 ± 00:53 mm:ss) performed five time-trials (i.e. 3, 4, 5, 10, 20 min) on a 400 m track, from which the mean power outputs were obtained through the Stryd Power Meter. An acceptable level of agreement was considered if the following criteria were met: low bias and standard error of the estimate (SEE) (&lt;14 W [values corresponding to the ±5% of the mean CP]; W′: &lt;2.0 kJ [values corresponding to the ±10% of the mean W′]), R2 &gt; 0.90, and ICC &gt; 0.81. The CP presented an acceptable SEE for CPwork (1.3 ± 0.5%) and CP1/time (2.7 ± 1.1%) when using the five time-trials. For both CP models, the 3–10 min was the shortest valid combination, whereas the 3–20, 4–20, and 5–20 min showed the greatest level of agreement. The W′ presented a high SEE for CPwork (14.1 ± 5.2%) and CP1/time (13.8 ± 6.2%) when using the five time-trials, therefore, none of the two time-trials combinations were considered. The CP parameter can be accurately estimated from different two time-trial combinations, whereas none reached an acceptable level of accuracy for the determination of W′.

Prediction of Exercise Tolerance in the Severe and Extreme Intensity Domains by a Critical Power Model.

This study aimed to assess the predictive capability of different critical power (CP) models on cycling exercise tolerance in the severe- and extreme-intensity domains. Nineteen cyclists (age: 23.0 ± 2.7 y) performed several time-to-exhaustion tests (Tlim) to determine CP, finite work above CP (W'), and the highest constant work rate at which maximal oxygen consumption was attained (IHIGH). Hyperbolic power-time, linear power-inverse of time, and work-time models with three predictive trials were used to determine CP and W'. Modeling with two predictive trials of the CP work-time model was also used to determine CP and W'. Actual exercise tolerance of IHIGH and intensity 5% above IHIGH (IHIGH+5%) were compared to those predicted by all CP models. Actual IHIGH (155 ± 30 s) and IHIGH+5% (120 ± 26 s) performances were not different from those predicted by all models with three predictive trials. Modeling with two predictive trials overestimated Tlim at IHIGH+5% (129 ± 33 s; p = 0.04). Bland-Altman plots of IHIGH+5% presented significant heteroscedasticity by all CP predictions, but not for IHIGH. Exercise tolerance in the severe and extreme domains can be predicted by CP derived from three predictive trials. However, this ability is impaired within the extreme domain.

An investigation of notch wear on the bull-nose tool during laser-assisted milling AerMet100 steel

Notch wear is one of the main tool failure modes when machining difficult-to-cut materials. Some experimental studies have confirmed that thermal-assisted machining is beneficial to suppress notch wear. However, the quantitative analysis model between the heating power and notch wear has not been reported. The stress distribution along the tool edge during laser-assisted milling with a bull-nose tool is calculated in this paper, and the influence of material softening is considered. Further, the critical laser power without notch wear generation is determined by calculating the stress concentration coefficient and comparing it with a critical value. A series of laser-assisted milling experiments are conducted to validate the critical laser power model, the results show the presented model is effective. Moreover, the effects of feed rate and radial cutting depth on the critical laser power are analyzed. This study can be further applied to optimize the laser power to minimize notch wear formation in the laser-assisted milling process.

Bayesian approaches for critical velocity modelling of data from intermittent efforts

In sports science, critical power and related critical velocity models have been widely investigated, and are being increasingly applied to field-based team sports. A challenge associated with these models is that laboratory experiments that yield accurate measurements of maximal sustainable velocity are expensive. Alternatively, inexpensive field data (from training and matches) are being used to fit such models. However, the intermittent nature of efforts in field-based sports implies that the dependent variable concerning maximum sustainable velocity is reliably calibrated only for short time durations. This paper develops methods where field data based on short time durations is combined with prior knowledge to fit the three-parameter critical velocity model. This is accomplished in a Bayesian framework for which Markov chain methods are required for model fitting and inference.

Comparison of parameters derived from a three-minute all-out test with classical benchmarks for running exercise.

This study aimed to compare four constructs from the three-minute all-out test (AO3)–end power (EP), the area above EP (WEP), maximum power (Pmax), and attained −to those derived from the classical CP model in tethered running. Seventeen male recreational runners underwent two experiments to test for reliability and agreement of AO3 parameters with those obtained from the classical CP model (Wꞌ and CP), a graded exercise test () and a 30-second all-out test (AO30s; Pmax); all performed on a non-motorized treadmill (NMT). Significance levels were set at p<0.05. There were no significant differences between test-retest for Pmax (p = 0.51), WEP (p = 0.39), and EP (p = 0.64), showing generally close to zero bias. Further, retest ICC were high for Pmax and EP (ICC > 0.86) but moderate for WEP (ICC = 0.69). Pmax showed no difference between AO3 and AO30s (p = 0.18; CV% = 9.5%). EP and WEP disagreed largely with their classical critical power model counterparts (p = 0.05; CV%>32.7% and p = 0.23; CV%>39.7%, respectively), showing greater error than their test-retest reliability. from AO3 was not different (p = 0.13) and well related (CV% = 8.4; ICC = 0.87) to the incremental test . Under the studied conditions, the agreement of EP and WEP to CP and Wꞌ was not strong enough to assure their use interchangeably. Pmax and were closer to their criterion parameters.

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

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. 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. On average, using the Skiba 2 model found end W' values different from zero (P = .007; mean ± 95% confidence limit, -2.7 ± 2.0kJ), 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 ± 2.5kJ), with 4 out of 8 cases falling within the predetermined error threshold. 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.

Methodological Reconciliation of CP and MLSS and Their Agreement with the Maximal Metabolic Steady State.

This study aimed to compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS. After a ramp test, 10 recreationally active males performed four to five severe-intensity constant-power output (PO) trials to estimate CP and three to four constant-PO trials to determine MLSS and identify MMSS. CP was computed using the three-parameter hyperbolic (CP3-hyp), two-parameter hyperbolic (CP2-hyp), linear (CPlin), and inverse of time (CP1/Tlim) models. In addition, the model with the lowest combined parameter error identified the "best-fit" CP (CPbest-fit). MLSS was determined as an increase in blood lactate concentration ≤1 mM during constant-PO cycling from the 5th (MLSS5-30), 10th (MLSS10-30), 15th (MLSS15-30), 20th (MLSS20-30), or 25th (MLSS25-30) to 30th minute. MMSS was identified as the greatest PO associated with the highest submaximal steady-state V˙O2 (MV˙O2ss). Concordance between the various CP and MLSS estimates was greatest when MLSS was identified as MLSS15-30, MLSS20-30, and MLSS25-30. The PO at MV˙O2ss was 243 ± 43 W. Of the various CP models and MLSS criteria, CP2-hyp (244 ± 46 W) and CPlin (248 ± 46 W) and MLSS15-30 and MLSS20-30 (both 245 ± 46 W), respectively, displayed, on average, the greatest agreement with MV˙O2ss. Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV˙O2ss. Differences between CP and MLSS can be reconciled with optimal methods of determination. When estimating MMSS, from CP the error margin of the model estimate should be considered. For MLSS, MLSS15-30 and MLSS20-30 demonstrated the highest degree of accuracy.

Application of V̇ o2 to the Critical Power Model to Derive the Critical V̇ o2.

Succi, PJ, Dinyer, TK, Byrd, MT, Voskuil, CC, and Bergstrom, HC. Application of V̇ o2 to the critical power model to derive the critical V̇ o2 . J Strength Cond Res 36(12): 3374-3380, 2022-The purposes of this study were to (a) determine whether the critical power (CP) model could be applied to V̇ o2 to estimate the critical V̇ o2 (CV̇ o2 ) and (b) to compare the CV̇ o2 with the V̇ o2 at CP (V̇ o2 CP), the ventilatory threshold (VT), respiratory compensation point (RCP), and the CV̇ o2 without the V̇ o2 slow component (CV̇ o2 slow). Nine subjects performed a graded exercise test to exhaustion to determine V̇ o2 peak, VT, and RCP. The subjects performed 4 randomized, constant power output work bouts to exhaustion. The time to exhaustion (T Lim ), the total work (W Lim ), and the total volume of oxygen consumed with (TV̇ o2 ) and without the slow component (TV̇ o2 slow) were recorded during each trial. The linear regressions of the TV̇ o2 vs. T Lim , TV̇ o2 slow vs. T Lim , and W Lim vs. T Lim relationship were performed to derive the CV̇ o2 , CV̇ o2 slow, and CP, respectively. A 1-way repeated-measures analysis of variance ( p ≤ 0.05) with follow-up Sidak-Bonferroni corrected pairwise comparisons indicated that CV̇ o2 (42.49 ± 3.22 ml·kg -1 ·min -1 ) was greater than VT (30.80 ± 4.66 ml·kg -1 ·min -1 ; p < 0.001), RCP (36.74 ± 4.49 ml·kg -1 ·min -1 ; p = 0.001), V̇ o2 CP (36.76 ± 4.31 ml·kg -1 ·min -1 ; p < 0.001), and CV̇ o2 slow (38.26 ± 2.43 ml·kg -1 ·min -1 ; p < 0.001). However, CV̇ o2 slow was not different than V̇ o2 CP ( p = 0.140) or RCP ( p = 0.235). Thus, the CP model can be applied to V̇ o2 to derive the CV̇ o2 and theoretically is the highest metabolic steady state that can be maintained for an extended period without fatigue. Furthermore, the ability of the CV̇ o2 to quantify the metabolic cost of exercise and the inefficiency associated with the V̇ o2 slow component may provide a valuable tool for researchers and coaches to examine endurance exercise.

Critical oxygenation: Can muscle oxygenation inform us about critical power?

The power-duration relationship is well documented for athletic performance and is formulated out mathematically in the critical power (CP) model. The CP model, when applied properly, has great predictive power, e.g. pedaling at a specific power output on an ergometer the model precisely calculates the time over which an athlete can sustain this power. However, CP presents physiological inconsistencies and process-oriented problems. The rapid development of near-infrared spectroscopy (NIRS) to measure muscle oxygenation (SmO2) dynamics provides a physiological exploration of the CP model on a conceptual and empirical level. Conceptually, the CP model provides two components: first CP is defined as the highest metabolic rate that can be achieved through oxidative means. And second, work capacity above CP named W’. SmO2 presents a steady-state in oxygen supply and demand and thereby represents CP specifically at a local level of analysis. Empirically, exploratory data quickly illustrates the relationship between performance and SmO2, as shown during 3-min all-out cycling tests to assess CP. During these tests, performance and SmO2 essentially mirror each other, and both CP and W’ generate solid correlation with what would be deemed their SmO2 counterparts: first, the steady-state of SmO2 correlates with CP. And second, the tissue oxygen reserve represented in SmO2, when calculated as an integral corresponds to W’. While the empirical data presented is preliminary, the proposition of a concurring physiological model to the current CP model is a plausible inference. Here we propose that SmO2 steady-state representing CP as critical oxygenation or CO. And the tissue oxygen reserve above CO would then be identified as O’. This new CO model could fill in the physiological gap between the highly predictive CP model and at times its inability to track human physiology consistently. For simplicity's sake, this would include acute changes in physiology as a result of changing climate or elevation with travel, which can affect performance. These types of acute fluctuations, but not limited to, would be manageable when applying a CO model in conjunction with the CP model. Further, modeling is needed to investigate the true potential of NIRS to model CP, with a focus on repeatability, recovery, and systemic vs local workloads.

Applications of the Critical Power Model to Dynamic Constant External Resistance Exercise: A Brief Review of the Critical Load Test

The study and application of the critical power (CP) concept has spanned many decades. The CP test provides estimates of two distinct parameters, CP and W′, that describe aerobic and anaerobic metabolic capacities, respectively. Various mathematical models have been used to estimate the CP and W′ parameters across exercise modalities. Recently, the CP model has been applied to dynamic constant external resistance (DCER) exercises. The same hyperbolic relationship that has been established across various continuous, whole-body, dynamic movements has also been demonstrated for upper-, lower-, and whole-body DCER exercises. The asymptote of the load versus repetition relationship is defined as the critical load (CL) and the curvature constant is L′. The CL and L′ can be estimated from the same linear and non-linear mathematical models used to derive the CP. The aims of this review are to (1) provide an overview of the CP concept across continuous, dynamic exercise modalities; (2) describe the recent applications of the model to DCER exercise; (3) demonstrate how the mathematical modeling of DCER exercise can be applied to further our understanding of fatigue and individual performance capabilities; and (4) make initial recommendations regarding the methodology for estimating the parameters of the CL test.

A comparative analysis of critical power models in elite road cyclists.

The aims of this study were to compare four different critical power model's ability to ascertain critical power and W′ in elite road cyclists, while making comparison to power output at respiratory compensation point, work rate (J·sec−1) at Wmax, and the work done above critical power during the Wmax test in relation to the W’. Ten male, elite endurance cyclists (V̇O2max ​= ​71.9 ​± ​5.9 ​ml ​kg−1·min−1) all familiar with critical power testing, participated in 3 testing sessions comprising 1. 15-s isokinetic (130 ​rpm) sprint, 1-min time trial, a ramp test to exhaustion, 2–3. a 4-min and/or 10-min self-paced maximal time trial separated by at least 24-h but limited to a 3-week period. The main findings show that all critical power models provided different W’ (F(1.061,8.486) ​= ​39.07, p ​= ​0.0002) and critical powers (F(1.022,8.179) ​= ​32.31, p ​= ​0.0004), while there was no difference between each model's critical power and power output at respiratory compensation point (F(1.155, 9.243) ​= ​2.72, p ​= ​0.131). Differences between models or comparisons with respiratory compensation point were deemed not clinically useful in the provision of training prescription or performance monitoring if the aim is to equal work rate at compensation point. There was also no post-hoc difference between work completed at Wmax (kJ) (p ​= ​0.890) and W′ using the nonlinear-3 model. Further research is required to investigate the physiological markers of intensity associated with respiratory compensation point and critical power work rate and the bioenergetic contribution to W’.

Influence of corners and road conditions on cycling individual time trial performance and ‘optimal’ pacing strategy: A simulation study

A mathematical model of a bike-rider’s longitudinal and lateral dynamics was used to study the influence of road conditions (tyre-road friction coefficient) on cycling individual time trial (ITT) performance and pacing strategy. A dynamic optimisation approach was used on different simulated 40-km-ITT courses, where environmental variables (i.e. slope and wind), the presence of corners and the tyre-road friction coefficient were varied. The objective of the optimisation was the performance time. Maximal velocity was constrained by road geometry and the tyre-road friction coefficient. The maximal deliverable power output was constrained accordingly to the critical power model. The simulation results suggest that when technical sections constitute 25% of the entire course, road conditions can meaningfully affect the final performance time and peak power required, but not the pacing strategy. In fact, the time lost in slow technical sections cannot be regained during fast straight sections, even if technical sections are used to restore anaerobic energy stores. However, more experimental research is needed to test the applicability of these predictions.

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.

Physiological and anthropometric determinants of critical power, W\u2032 and the reconstitution of W\u2032 in trained and untrained male cyclists

PurposeThis study examined the relationship of physiological and anthropometric characteristics with parameters of the critical power (CP) model, and in particular the reconstitution of W′ following successive bouts of maximal exercise, amongst trained and untrained cyclists.MethodsTwenty male adults (trained nine; untrained 11; age 39 ± 15 year; mass 74.7 ± 8.7 kg; V̇O2max 58.0 ± 8.7 mL kg−1 min−1) completed three incremental ramps (20 W min−1) to exhaustion interspersed with 2-min recoveries. Pearson’s correlation coefficients were used to assess relationships for W′ reconstitution after the first recovery (W′rec1), the delta in W′ reconstituted between recoveries (∆W′rec), CP and W′.ResultsCP was strongly related to V̇O2max for both trained (r = 0.82) and untrained participants (r = 0.71), whereas W′ was related to V̇O2max when both groups were considered together (r = 0.54). W′rec1 was strongly related to V̇O2max for the trained (r = 0.81) but not untrained (r = 0.18); similarly, ∆W′rec was strongly related to V̇O2max (r = − 0.85) and CP (r = − 0.71) in the trained group only.ConclusionsNotable physiological relationships between parameters of aerobic fitness and the measurements of W′ reconstitution were observed, which differed among groups. The amount of W′ reconstitution and the maintenance of W′ reconstitution that occurred with repeated bouts of maximal exercise were found to be related to key measures of aerobic fitness such as CP and V̇O2max. This data demonstrates that trained cyclists wishing to improve their rate of W′ reconstitution following repeated efforts should focus training on improving key aspects of aerobic fitness such as V̇O2max and CP.