The Training Intensity Distribution of Marathon Runners Across Performance Levels

BACKGROUND

The training characteristics and training intensity distribution (TID) of elite athletes have been extensively studied, but a comprehensive analysis of the TID across runners from different performance levels is lacking.

METHODS

Training sessions from the 16 weeks preceding 151,813 marathons completed by 119,452 runners were analysed. The TID was quantified using a three-zone approach (Z1, Z2 and Z3), where critical speed defined the boundary between Z2 and Z3, and the transition between Z1 and Z2 was assumed to occur at 82.3% of critical speed. Training characteristics and TID were reported based on marathon finish time.

RESULTS

Training volume across all runners was 45.1 ± 26.4 km·week-1, but the fastest runners within the dataset (marathon time 120-150 min) accumulated > three times more volume than slower runners. The amount of training time completed in Z2 and Z3 running remained relatively stable across performance levels, but the proportion of Z1 was higher in progressively faster groups. The most common TID approach was pyramidal, adopted by > 80% of runners with the fastest marathon times. There were strong, negative correlations (p < 0.01, R2 ≥ 0.90) between marathon time and markers of training volume, and the proportion of training volume completed in Z1. However, the proportions of training completed in Z2 and Z3 were correlated (p < 0.01, R2 ≥ 0.85) with slower marathon times.

CONCLUSION

The fastest runners in this dataset featured large training volumes, achieved primarily by increasing training volume in Z1. Marathon runners adopted a pyramidal TID approach, and the prevalence of pyramidal TID increased in the fastest runners.

Analysis of the factors influencing the proximity and agreement between critical power and maximal lactate steady state: a systematic review and meta-analyses

Identifying the boundary between heavy and severe exercise domains is crucial since it demarcates the transition from sustainable to unsustainable exercise. This systematic review aimed to determine differences and agreement between two indices used to determine this boundary, namely critical power (CP) and maximal lactate steady state (MLSS), and how moderators may affect these differences. Ten out of 782 studies found were included in the meta analyses. Random effect meta-analyses were performed to evaluate the mean differences (MD) between CP and MLSS, and moderators' effect on MD was assessed using meta-regression. CP and MLSS agreement was tested using Bland-Altman meta-analyses on the limits of agreements (LoA) of the MD. Power output (PO) at CP was higher (MD (95% LoA) = 12.42 [-19.23; 44.08] W, p = 0.005) than PO at MLSS, with no differences between CP and MLSS in terms of oxygen uptake (MD (95% LoA) = 0.09 [-0.34; 0.52] L⋅min-1, p = 0.097), heart rate (MD (95% LoA) = 0.61 [-15.84; 17.05] bpm, p = 0.784), and blood lactate concentration (MD (95% LoA) = 1.63 [-2.85; 6.11] mM, p = 0.240). Intensities at CP (p = 0.002) and MLSS (p = 0.010) influenced the MD expressed in W. In conclusion, solely when expressed in PO, CP was higher than MLSS, with larger differences in fitter and younger individuals, emphasizing the possible effect of the indicators used for assessing exercise intensity. Finally, the high interindividual variability observed in the differences between CP and MLSS could compromise their interchangeability in predicting the heavy to severe boundary regardless of the parameter used to assess exercise intensity.

Field-based tests for determining critical speed among runners and its practical application: a systematic review

Introduction

This review focuses exclusively on field-based critical speed (CS) tests for runners, aiming to evaluate key testing conditions to optimize field-based assessments and their practical applications.

Methods

A systematic search was conducted in PubMed, Scopus, SPORTDiscus, and Web of Science databases in July 2024 using terms like "critical power," "critical speed," "testing," and "field condition" along with related keywords. Following PRISMA 2020 guidelines, studies were systematically identified, screened, assessed for eligibility, and evaluated for the validity, reliability, and applicability of field-based methods for determining CS in runners.

Results

From an initial pool of 450 studies, 19 met the inclusion criteria. The time trial (TT) test and the 3-minute all-out test (3MT) emerged as the most frequently used field-based methods, demonstrating high reliability when conducted under specific conditions.

Conclusion

This review demonstrates that while field-based CS testing is a practical alternative to lab-based assessments, obtaining reliable results relies on following recommended testing settings, particularly for TT tests. By outlining the practical applications and conditions necessary for accurate CS assessment, this review supports athletes and coaches in applying CS testing effectively to enhance training strategies and performance.

Testing the predictive capacity of a muscle fatigue model on electrically stimulated adductor pollicis

PURPOSE

Based on the critical power (Pc or critical force; Fc) concept, a recent mathematical model formalised the proportional link between the decrease in maximal capacities during fatiguing exercises and the amount of impulse accumulated above Fc. This study aimed to provide experimental support to this mathematical model of muscle fatigability in the severe domain through testing (i) the model identifiability using non-exhausting tests and (ii) the model ability to predict time to exhaustion (tlim) and maximal force (Fmax) decrease.

METHODS

The model was tested on eight participants using electrically stimulated adductor pollicis muscle force. The Fmax was recorded every 15 s for all tests, including five constant tests to estimate the initial maximal force (Fi), Fc, and a time constant (τ). The model's parameters were used to compare the predicted and observed tlim values of the incremental ramp test and Fmax(t) of the sine test.

RESULTS

The results showed that the model accurately estimated Fi, Fc, and τ (CI95% = 2.7%Fi and 9.1 s for Fc and τ, respectively; median adjusted r2 = 0.96) and predicted tlim and Fmax with low systematic and random errors (11 ± 20% and - 1.8 ± 7.7%Fi, respectively).

CONCLUSION

This study revealed the potential applications of a novel mathematical formalisation that encompasses previous research on the critical power concept. The results indicated that the model's parameters can be determined from non-exhaustive tests, as long as maximal capacities are regularly assessed. With these parameters, the evolution of maximal capacities (i.e. fatigability) at any point during a known exercise and the time to exhaustion can be accurately predicted.

Power or speed: Which metric is more accurate for modelling endurance running performance on track?

This study aimed to compare the accuracy of the power output, measured by a power meter, with respect to the speed, measured by an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) sport watch to determine the critical power (CP) and speed (CS), work over CP (W') and CS (D'), and long-duration performance (i.e., 60 min). Fifteen highly trained athletes randomly performed seven time trials on a 400 m track. The CP/CS and W'/D' were defined through the inverse of time model using the 3, 4, 5, 10, and 20 min trials. The 60 min performance was estimated through the power law model using the 1, 3, and 10 min trials and compared with the actual performance. A lower standard error of the estimate was obtained when using the power meter (CP: 2.7 [2.1-3.3] % and W': 13.8 [10.4-17.3] %) compared to the speed reported by the IMU (CS: 3.4 [2.5-4.3] %) and D': 20.7 [16.6-24.7] %) and GNSS sport watch (CS: 3.4 [2.5-4.3] % and D': 20.6 [16.7-24.7] %). A lower coefficient of variation was also observed for the power meter (4.9 [3.7-6.1] %) Regarding the speed reported by the IMU (10.9 [7.1-14.8] %) and GNSS sport watch (10.9 [7.0-14.7] %) in the 60 min performance estimation, the power meter offered lower errors than the IMU and GNSS sport watch for modelling endurance performance on the track.

Critical Power and Maximal Lactate Steady State in Cycling: "Watts" the Difference?

From a physiological perspective, the delineation between steady-state and non-steady-state exercise, also referred to as the maximal metabolic steady state, holds paramount importance for evaluating athletic performance and designing and monitoring training programs. The critical power and the maximal lactate steady state are two widely used indices to estimate this threshold, yet previous studies consistently reported significant discrepancies between their associated power outputs. These findings have fueled the debate regarding the interchangeability of critical power and the maximal lactate steady state in practice. This paper reviews the methodological intricacies intrinsic to the determination of these thresholds, and elucidates how inappropriate determination methods and methodological inconsistencies between studies have contributed to the documented differences in the literature. Through a critical examination of relevant literature and by integration of our laboratory data, we demonstrate that differences between critical power and the maximal lactate steady state may be reconciled to only a few Watts when applying appropriate and strict determination criteria, so that both indices may be used to estimate the maximal metabolic steady-state threshold in practice. To this end, we have defined a set of good practice guidelines to assist scientists and coaches in obtaining the most valid critical power and maximal lactate steady state estimates.

Reliability and Validity of Predicted Performance in the Severe-Intensity Domain From the 3-Minute All-Out Running Test

PURPOSE

The aim of this study was to analyze the reliability and validity of the predicted distance-time relationship in the severe-intensity domain from a 3-minute all-out running test (3MT).

METHODS

Twelve runners performed two 3MTs (test #1 and test #2) on an outdoor 400-m track after familiarization. Eighteen-hertz Global Positioning System data were used to estimate critical speed (CS) and distance covered above CS (D'). Time to cover 1200 and 3600 m (T1200 and T3600, respectively) was predicted using CS and D' estimates from each 3MT. Eight runners performed 2 time trials in a single visit to assess real T1200 and T3600. Intraclass correlation coefficients (ICCs) and standard errors of measurement were calculated for reliability analysis.

RESULTS

Good to excellent reliability was found for CS, T1200, and T3600 estimates from 3MT (ICC > .95, standard error of measurement between 1.3% and 2.2%), and poor reliability was found for D' (ICC = .55, standard error of measurement = 27%). Predictions from 3MT were significantly correlated to actual T1200 (r = .87 and .85 for test #1 and test #2, respectively) and T3600 (r = .91 and .82 for test #1 and test #2, respectively). The calculation of error prediction showed a systematic error between predicted and real T3600 (6.4% and 7.8% for test #1 and test #2, respectively, P < .01) contrary to T1200 (P > .1). Random error was between 4.4% and 6.1% for both distances.

CONCLUSIONS

Despite low reliability of D', 3MT yielded a reliable predicted distance-time relationship allowing repeated measures to evidence change with training adaptation. However, caution should be taken with prediction of performance potential of a single individual because of substantial random error and significant underestimation of T3600.

Agreement Between Maximal Lactate Steady State and Critical Power in Different Sports: A Systematic Review and Bayesian's Meta-Regression

ABSTRACT

Borszcz, FK, de Aguiar, RA, Costa, VP, Denadai, BS, and de Lucas, RD. Agreement between maximal lactate steady state and critical power in different sports: A systematic review and Bayesian's meta-regression. J Strength Cond Res 38(6): e320-e339, 2024-This study aimed to systematically review the literature and perform a meta-regression to determine the level of agreement between maximal lactate steady state (MLSS) and critical power (CP). Considered eligible to include were peer-reviewed and "gray literature" studies in English, Spanish, and Portuguese languages in cyclical exercises. The last search was made on March 24, 2022, on PubMed, ScienceDirect, SciELO, and Google Scholar. The study's quality was evaluated using 4 criteria adapted from the COSMIN tool. The level of agreement was examined by 2 separate meta-regressions modeled under Bayesian's methods, the first for the mean differences and the second for the SD of differences. The searches yielded 455 studies, of which 36 studies were included. Quality scale revealed detailed methods and small samples used and that some studies lacked inclusion/exclusion criteria reporting. For MLSS and CP comparison, likely (i.e., coefficients with high probabilities) covariates that change the mean difference were the MLSS time frame and delta criteria of blood lactate concentration, MLSS number and duration of pauses, CP longest predictive trial duration, CP type of predictive trials, CP model fitting parameters, and exercise modality. Covariates for SD of the differences were the subject's maximal oxygen uptake, CP's longest predictive trial duration, and exercise modality. Traditional MLSS protocol and CP from 2- to 15-minute trials do not reflect equivalent exercise intensity levels; the proximity between MLSS and CP measures can differ depending on test design, and both MLSS and CP have inherent limitations. Therefore, comparisons between them should always consider these aspects.

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.

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 Collapse

Key Words

• All-out
• Anaerobic capacities
• Critical power
• Endurance
• Exercise fatigability
• Fatigue
• Maximal force capacity
• Muscular capacities
• Power-time relationship
• Time to exhaustion

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MESH Headings

• Exercise/physiology
• Muscle Fatigue
• Muscles/physiology
• Models, Theoretical
• Muscle, Skeletal/physiology

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Grants Collapse

Affiliation(s)

M Bowen Laboratoire Interuniversitaire de Biologie de la Motricité LIBM, EA 7424, Savoie Mont Blanc University, F-7300, Chambéry, France.
P Samozino Laboratoire Interuniversitaire de Biologie de la Motricité LIBM, EA 7424, Savoie Mont Blanc University, F-7300, Chambéry, France
M Vonderscher Laboratoire Interuniversitaire de Biologie de la Motricité LIBM, EA 7424, Savoie Mont Blanc University, F-7300, Chambéry, France
D Dutykh Mathematics Department, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates; Causal Dynamics Pty Ltd, WA 6009, Perth, Australia
B Morel Laboratoire Interuniversitaire de Biologie de la Motricité LIBM, EA 7424, Savoie Mont Blanc University, F-7300, Chambéry, France

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Reliability and validity of a non-linear index of heart rate variability to determine intensity thresholds

Exercise intensity distribution is crucial for exercise individualization, prescription, and monitoring. As traditional methods to determine intensity thresholds present limitations, heart rate variability (HRV) using DFA a1 has been proposed as a biomarker for exercise intensity distribution. This index has been associated with ventilatory and lactate thresholds in previous literature. This study aims to assess DFA a1's reliability and validity in determining intensity thresholds during an incremental cycling test in untrained healthy adults. Sixteen volunteers (13 males and 3 females) performed two identical incremental cycling stage tests at least 1 week apart. First and second ventilatory thresholds, lactate thresholds, and HRV thresholds (DFA a1 values of 0.75 and 0.5 for HRVT1 and HRVT2, respectively) were determined in heart rate (HR), relative oxygen uptake (VO2rel), and power output (PO) values for both tests. We used intraclass correlation coefficient (ICC), change in mean, and typical error for the reliability analysis, and paired t-tests, correlation coefficients, ICC, and Bland-Altman analysis to assess the agreement between methods. Regarding reliability, HRV thresholds showed the best ICCs when measured in PO (HRVT1: ICC = .87; HRVT2: ICC = .97), comparable to ventilatory and lactate methods. HRVT1 showed the strongest agreement with LA 2.5 in PO (p = 0.09, r = .93, ICC = .93, bias = 9.9 ± 21.1), while HRVT2 reported it with VT2 in PO (p = 0.367, r = .92, ICC = .92, bias = 5.3 ± 21.9). DFA a1 method using 0.75 and 0.5 values is reliable and valid to determine HRV thresholds in this population, especially in PO values.

Vascular dysfunction and the age-related decline in critical power

Ageing results in lower exercise tolerance, manifested as decreased critical power (CP). We examined whether the age-related decrease in CP occurs independently of changes in muscle mass and whether it is related to impaired vascular function. Ten older (63.1 ± 2.5 years) and 10 younger (24.4 ± 4.0 years) physically active volunteers participated. Physical activity was measured with accelerometry. Leg muscle mass was quantified with dual X-ray absorptiometry. The CP and maximum power during a graded exercise test (PGXT ) of single-leg knee-extension exercise were determined over the course of four visits. During a fifth visit, vascular function of the leg was assessed with passive leg movement (PLM) hyperaemia and leg blood flow and vascular conductance during knee-extension exercise at 10 W, 20 W, slightly below CP (90% CP) and PGXT . Despite not differing in leg lean mass (P = 0.901) and physical activity (e.g., steps per day, P = 0.735), older subjects had ∼30% lower mass-specific CP (old = 3.20 ± 0.94 W kg-1 vs. young = 4.60 ± 0.87 W kg-1 ; P < 0.001). The PLM-induced hyperaemia and leg blood flow and/or conductance were blunted in the old at 20 W, 90% CP and PGXT (P < 0.05). When normalized for leg muscle mass, CP was strongly correlated with PLM-induced hyperaemia (R2  = 0.52; P < 0.001) and vascular conductance during knee-extension exercise at 20 W (R2  = 0.34; P = 0.014) and 90% CP (R2  = 0.39; P = 0.004). In conclusion, the age-related decline in CP is not only an issue of muscle quantity, but also of impaired muscle quality that corresponds to impaired vascular function.

Running Functional Threshold versus Critical Power: Same Concept but Different Values

The aims of this study were (i) to estimate the functional threshold power (FTP) and critical power (CP) from single shorter time trials (TTs) (i. e. 10, 20 and 30 minutes) and (ii) to assess their location in the power-duration curve. Fifteen highly trained athletes randomly performed ten TTs (i. e. 1, 2, 3, 4, 5, 10, 20, 30, 50 and 60 minutes). FTP was determined as the mean power output developed in the 60-min TT, while CP was estimated in the running power meter platform according to the manufacturer's recommendations. The linear regression analysis revealed an acceptable FTP estimate for the 10, 20 and 30-min TTs (SEE≤12.27 W) corresponding to a correction factor of 85, 90 and 95%, respectively. An acceptable CP estimate was only observed for the 20-min TT (SEE=6.67 W) corresponding to a correction factor of 95%. The CP was located at the 30-min power output (1.0 [-5.1 to 7.1] W), which was over FTP (14 [7.0 to 21] W). Therefore, athletes and practitioners concerned with determining FTP and CP through a feasible testing protocol are encouraged to perform a 20-min TT and apply a correction factor of 90 and 95%, respectively.

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.

Remote Determination of Critical Speed and Critical Power in Recreational Runners

PURPOSE

This study aimed to compare estimations of critical speed (CS) and work completed above CS (D'), and their analogies for running power (critical power [CP] and W'), derived from raw data obtained from habitual training (HAB) and intentional maximal efforts in the form of time trials (TTs) and 3-minute all-out tests (3MTs) in recreational runners. The test-retest reliability of the 3MT was further analyzed.

METHODS

Twenty-three recreational runners (4 female) used a foot pod to record speed, altitude, and power output for 8 consecutive weeks. CS and D', and CP and W', were calculated from the best 3-, 7-, and 12-minute segments recorded in the first 6 weeks of their HAB and in random order in weeks 7 and 8 from 3 TTs (3, 7, and 12 min) and three 3MTs (to assess test-retest reliability).

RESULTS

There was no difference between estimations of CS or CP derived from HAB, TT, and 3MT (3.44 [0.63], 3.42 [0.53], and 3.76 [0.57] m · s-1 and 281 [41], 290 [45], and 305 [54] W, respectively), and strong agreement between HAB and TT for CS (r = .669) and CP (r = .916). Limited agreement existed between estimates of D'/W'. Moderate reliability of D'/W' was demonstrated between the first and second 3MTs, whereas excellent reliability was demonstrated for CS/CP.

CONCLUSION

These data suggest that estimations of CS/CP can be derived remotely, from either HAB, TT, or 3MT, although the lower agreement between D'/W' warrants caution when using these measures interchangeably.

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.

Sex Differences in the Speed-Duration Relationship of Elite Runners across the Lifespan

PURPOSE

To determine if the speed-duration relationship is altered with age and sex of elite Master's runners.

METHODS

The world's top 10 performances for men and women in three events (800, 1500, and 5000 m) across six age groups (18-34 yr, 40-49 yr, 50-59 yr, 60-69 yr, 70-79 yr, and 80-89 yr) were analyzed from public data to establish theoretical models of the speed-duration relationship. Critical speed (CS) and the curvature constant ( D ') were estimated by fitting the average speeds and performance times with a two-parameter hyperbolic model.

RESULTS

Critical speed expressed relative to the 18- to 34-yr-olds, declined with age (92.2% [40-49] to 55.2% [80-89]; P < 0.001), and absolute CS was higher in men than women within each age group ( P < 0.001). The percent difference in CS between the men and women progressively increased across age groups (10.8% [18-34] to 15.5% [80-89]). D ' was lower in women than men in the 60-69 yr, 70-79 yr, and 80-89 yr age groups ( P < 0.001), but did not differ in the 18-34 yr, 40-49 yr, or 50-59 yr age groups.

CONCLUSIONS

Critical speed progressively decreased with age, likely due to age-related decrements in several physiological systems that cause reduced aerobic capacity. The mechanism for the larger sex difference in CS in the older age groups is unknown but may indicate physiological differences that occur with aging and/or historical sociological factors that have reduced participation opportunities of older female runners resulting in a more limited talent pool.

Variability in exercise tolerance and physiological responses to exercise prescribed relative to physiological thresholds and to maximum oxygen uptake

NEW FINDINGS

What is the central question of this study? Does prescribing exercise intensity using physiological thresholds create a more homogeneous exercise stimulus than using traditional intensity anchors? What is the main finding and its importance? Prescribing exercise using physiological thresholds, notably critical power, reduced the variability in exercise tolerance and acute metabolic responses. At higher intensities, approaching or exceeding the transition from heavy to severe intensity exercise, the imprecision of using fixed %V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$  as an intensity anchor becomes amplified.

ABSTRACT

The objective of this study was to determine whether the variability in exercise tolerance and physiological responses is lower when exercise is prescribed relative to physiological thresholds (THR) compared to traditional intensity anchors (TRAD). Ten individuals completed a series of maximal exercise tests and a series of moderate (MOD), heavy (HVY) and severe intensity (HIIT) exercise bouts prescribed using THR intensity anchors (critical power and gas exchange threshold) and TRAD intensity anchors (maximum oxygen uptake;V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$ ). There were no differences in exercise tolerance or acute response variability between MODTHR and MODTRAD . All individuals completed HVYTHR but only 30% completed HVYTRAD . Compared to HVYTHR , where work rates were all below critical power, work rates in HVYTRAD exceeded critical power in 70% of individuals. There was, however, no difference in acute response variability between HVYTHR and HVYTRAD . All individuals completed HIITTHR but only 20% completed HIITTRAD . The variability in peak (F = 0.274) and average (F = 0.318) blood lactate responses was lower in HIITTHR compared to HIITTRAD . The variability in W' depletion (the finite work capacity above critical power) after the final interval bout was lower in HIITTHR compared to HIITTRAD (F = 0.305). Using physiological thresholds to prescribe exercise intensity reduced the heterogeneity in exercise tolerance and physiological responses to exercise spanning the boundary between the heavy and severe intensity domains. To increase the precision of exercise intensity prescription, it is recommended that, where possible, physiological thresholds are used in place ofV ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$ .

Modeling the Power-Duration Relationship in Professional Cyclists During the Giro d'Italia

ABSTRACT

Vinetti, G, Pollastri, L, Lanfranconi, F, Bruseghini, P, Taboni, A, and Ferretti, G. Modeling the power-duration relationship in professional cyclists during the Giro d'Italia. J Strength Cond Res 37(4): 866-871, 2023-Multistage road bicycle races allow the assessment of maximal mean power output (MMP) over a wide spectrum of durations. By modeling the resulting power-duration relationship, the critical power ( CP ) and the curvature constant ( W' ) can be calculated and, in the 3-parameter (3-p) model, also the maximal instantaneous power ( P0 ). Our aim is to test the 3-p model for the first time in this context and to compare it with the 2-parameter (2-p) model. A team of 9 male professional cyclists participated in the 2014 Giro d'Italia with a crank-based power meter. The maximal mean power output between 10 seconds and 10 minutes were fitted with 3-p, whereas those between 1 and 10 minutes with the 2- model. The level of significance was set at p < 0.05. 3-p yielded CP 357 ± 29 W, W' 13.3 ± 4.2 kJ, and P0 1,330 ± 251 W with a SEE of 10 ± 5 W, 3.0 ± 1.7 kJ, and 507 ± 528 W, respectively. 2-p yielded a CP and W' slightly higher (+4 ± 2 W) and lower (-2.3 ± 1.1 kJ), respectively ( p < 0.001 for both). Model predictions were within ±10 W of the 20-minute MMP of time-trial stages. In conclusion, during a single multistage racing event, the 3-p model accurately described the power-duration relationship over a wider MMP range without physiologically relevant differences in CP with respect to 2-p, potentially offering a noninvasive tool to evaluate competitive cyclists at the peak of training.

Interaction of Factors Determining Critical Power

The physiological determinants of high-intensity exercise tolerance are important for both elite human performance and morbidity, mortality and disease in clinical settings. The asymptote of the hyperbolic relation between external power and time to task failure, critical power, represents the threshold intensity above which systemic and intramuscular metabolic homeostasis can no longer be maintained. After ~ 60 years of research into the phenomenon of critical power, a clear understanding of its physiological determinants has emerged. The purpose of the present review is to critically examine this contemporary evidence in order to explain the physiological underpinnings of critical power. Evidence demonstrating that alterations in convective and diffusive oxygen delivery can impact upon critical power is first addressed. Subsequently, evidence is considered that shows that rates of muscle oxygen utilisation, inferred via the kinetics of pulmonary oxygen consumption, can influence critical power. The data reveal a clear picture that alterations in the rates of flux along every step of the oxygen transport and utilisation pathways influence critical power. It is also clear that critical power is influenced by motor unit recruitment patterns. On this basis, it is proposed that convective and diffusive oxygen delivery act in concert with muscle oxygen utilisation rates to determine the intracellular metabolic milieu and state of fatigue within the myocytes. This interacts with exercising muscle mass and motor unit recruitment patterns to ultimately determine critical power.

The exercise power-duration relationship is equally reproducible in eumenorrheic female and male humans

This study aims to investigate the effect of the menstrual cycle (MC) on exercise performance across the power-duration relationship (PDR). We hypothesized females would exhibit greater variability in the PDR across the MC than males across a similar timespan, with critical power (CP) and work-prime (W') being lower during the early follicular phase than the late follicular and midluteal phases. Seven eumenorrheic, endurance-trained female adults performed multiple constant-load-to-task-failure and maximum-power tests at three timepoints across the MC (early follicular, late follicular, and midluteal phases). Ten endurance-trained male adults performed the same tests approximately 10 days apart. No differences across the PDR were observed between MC phases (CP: 186.74 ± 31.00 W, P = 0.955, CV = 0.81 ± 0.65%) (W': 7,961.81 ± 2,537.68 J, P = 0.476, CV = 10.48 ± 3.06%). CP was similar for male and female subjects (11.82 ± 1.42 W·kg^-1 vs. 11.56 ± 1.51 W·kg^-1, respectively) when controlling for leg lean mass. However, W' was larger (P = 0.047) for male subjects (617.28 ± 130.10 J·kg^-1) than female subjects (490.03 ± 136.70 J·kg^-1) when controlling for leg lean mass. MC phase does not need to be controlled when conducting aerobic endurance performance research on eumenorrheic female subjects without menstrual dysfunction. Nevertheless, several sex differences in the power-duration relationship exist, even after normalizing for body composition. Therefore, previous studies describing the physiology of exercise performance in male subjects may not perfectly describe that of female subjects.NEW & NOTEWORTHY Females are often excluded from exercise performance research due to experimental challenges in controlling for the menstrual cycle (MC), causing uncertainty regarding how the MC impacts female performance. The present study examined the influences that biological sex and the MC have on the power-duration relationship (PDR) by comparing critical power (CP), Work-prime (W'), and maximum power output (P_MAX) in males and females. Our data provide evidence that the MC does not influence the PDR and that females exhibit similar reproducibility as males. Thus, when conducting aerobic endurance exercise research on eumenorrheic females without menstrual dysfunction, the phase of the MC does not need to be controlled. Although differences in body composition account for some differences between the sexes, sex differences in W' and P_MAX persisted even after normalizing for different metrics of body composition. These data highlight the necessity and feasibility of examining sex differences in performance, as previously generated male-only data within the literature may not apply to female subjects.

Influence and Mechanisms of Action of Environmental Stimuli on Work Near and Above the Severe Domain Boundary (Critical Power)

Abstract

The critical power (CP) concept represents the uppermost rate of steady state aerobic metabolism during work. Work above CP is limited by a fixed capacity (W′) with exercise intensity being an accelerant of its depletion rate. Exercise at CP is a considerable insult to homeostasis and any work done above it will rapidly become intolerable. Humans live and exercise in situations of hypoxia, heat, cold and air pollution all of which impose a new environmental stress in addition to that of exercise. Hypoxia disrupts the oxygen cascade and consequently aerobic energy production, whereas heat impacts the circulatory system’s ability to solely support exercise performance. Cold lowers efficiency and increases the metabolic cost of exercise, whereas air pollution negatively impacts the respiratory system. This review will examine the effects imposed by environmental conditions on CP and W′ and describe the key physiological mechanisms which are affected by the environment.

Graphical Abstract

Physiological and Perceptual Responses in Spinal Cord Injury Handcyclists During an Endurance Interval Training: The Role of Critical Speed

OBJECTIVE

This study sought to determine the physiological (oxygen uptake, heart rate, and blood lactate concentration) and perceptual (rating of perceived exertion) responses during an endurance interval training at the critical speed in elite handcyclists with spinal cord injury.

DESIGN

Eight handcyclists performed an incremental test, three tests to exhaustion at a constant speed to determine critical speed, and the endurance interval training. The endurance interval training consisted of 6 × 5 mins at the individualized critical speed, with passive recovery of 50 secs. All testing was performed using their own handcycles on an oversized motorized treadmill. Physiological and perceptual responses were assessed during the incremental and endurance interval training tests.

RESULTS

There was no significant difference in average oxygen uptake from the first to the sixth repetition. The mean ∆[La-]10_last between the 10th to the 30th minute of the exercise was -0.36 mmol·l-1, and no difference was detected from the first to the sixth repetition. The heart rate also remained stable during endurance interval training, whereas rating of perceived exertion increased significantly throughout the session.

CONCLUSIONS

Repetitions of 5 mins at the critical speed in elite handcyclists are associated with cardiorespiratory and lactate steady state, whereas the perceived exertion increased systematically.

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.

Elite versus non-elite cyclist - Stepping up to the international/elite ranks from U23 cycling

This study investigated the physiological, performance and training characteristics of U23 cyclists and assessed the requirements of stepping up to the elite/international ranks. Twenty highly trained U23 cyclists (age, 22.1 ± 0.8 years; body mass, 69.1 ± 6.8 kg; VO_2max, 76.1 ± 3.9 ml·kg^-1·min^-1) participated in this study. The cyclists were a posteriori divided into two groups based on whether or not they stepped up to elite/international level cycling (U23_ELITE vs. U23_NON-ELITE). Physiological, performance and training and racing characteristics were determined and compared between groups. U23_ELITE demonstrated higher absolute peak power output (p = .016), 2 min (p = .026) 5 min (p = .042) and 12 min (p ≤ .001) power output as well as higher absolute critical power (p = .002). Further, U23_ELITE recorded more accumulated hours (p ≤ .001), covered distance (p ≤ .001), climbing metres (p ≤ .001), total sessions (p ≤ .001), total work (p ≤ .001) and scored more UCI points (p ≤ .001). These findings indicate that U23_ELITE substantially differed from U23_NON-ELITE regarding physiological, performance and training and racing characteristics derived from laboratory and field. These variables should be considered by practitioners supporting young cyclists throughout their development towards the elite/international ranks.

Application of the Force-velocity-power Concept to the 3-Min all-out Running Test

Force-velocity-power (FVP) profiling offers insights related to key factors that may enhance or hinder sprinting performances. Whether the same FVP principles could be applied to the sprinting portion of the 3-minute all-out test for running (3MT) has not been previously investigated. Twenty moderately trained participants volunteered for the study (age: 24.75 ± 3.58 yrs; height: 1.69±0.11 m; mass: 73.74±12.26 kg). After familiarization of all testing procedures, participants completed: (i) a 40-m all-out sprint test, and (ii) a 3MT. Theoretical maximal force and power, but not velocity, were significantly higher for the 40-m sprint test. Most FVP variables from the two tests were weakly to moderately correlated, with the exception of maximal velocity. Finally, maximal velocity and relative peak power were predictive of D', explaining approximately 51% of the variance in D'. Although similar maximal velocities are attained during both the 40-m sprint and the 3MT, the underlying mechanisms are markedly different. The FVP parameters obtained from either test are likely not interchangeable but do provide valuable insights regarding the potential mechanisms by which D' may be improved.

Performance prediction, pacing profile and running pattern of elite 1-h track running events

Purpose

This study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times.

Methods

The individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern.

Results

Regardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PLV20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed.

Conclusions

The PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events.

Effects of Flat and Uphill Cycling on the Power-duration Relationship

The purpose of this study was to investigate the effects of flat and uphill cycling on critical power and the work available above critical power. Thirteen well-trained endurance athletes performed three prediction trials of 10-, 4- and 1-min in both flat (0.6%) and uphill (9.8%) cycling conditions on two separate days. Critical power and the work available above critical power were estimated using various mathematical models. The best individual fit was used for further statistical analyses. Paired t-tests and Bland-Altman plots with 95% limits of agreement were applied to compare power output and parameter estimates between cycling conditions. Power output during the 10- and 4-min prediction trial and power output at critical power were not significantly affected by test conditions (all at p>0.05), but the limits of agreement between flat and uphill cycling power output and critical power estimates are too large to consider both conditions as equivalent. However, power output during the 1-min prediction trial and the work available above critical power were significantly higher during uphill compared to flat cycling (p<0.05). The results of this investigation indicate that gradient affects cycling time-trial performance, power output at critical power, and the amount of work available above critical power.

Decoupling of Internal and External Workload During a Marathon: An Analysis of Durability in 82,303 Recreational Runners

Aim

This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance.

Methods

The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Internal workload was determined as a percentage of maximum heart rate, and external workload as speed relative to estimated critical speed (CS). Decoupling magnitude (i.e., decoupling in the 35–40 km segment relative to the 5–10 km segment) was classified as low (< 1.1), moderate (≥ 1.1 but < 1.2) or high (≥ 1.2). Decoupling onset was calculated when decoupling exceeded 1.025.

Results

The overall internal-to-external workload decoupling experienced was 1.16 ± 0.22, first detected 25.2 ± 9.9 km into marathon running. The low decoupling group (34.5% of runners) completed the marathon at a faster relative speed (88 ± 6% CS), had better marathon performance (217.3 ± 33.1 min), and first experienced decoupling later in the marathon (33.4 ± 9.0 km) compared to those in the moderate (32.7% of runners, 86 ± 6% CS, 224.9 ± 31.7 min, and 22.6 ± 7.7 km), and high decoupling groups (32.8% runners, 82 ± 7% CS, 238.5 ± 30.7 min, and 19.1 ± 6.8 km; all p < 0.01). Compared to females, males’ decoupling magnitude was greater (1.17 ± 0.22 vs. 1.12 ± 0.16; p < 0.01) and occurred earlier (25.0 ± 9.8 vs. 26.3 ± 10.6 km; p < 0.01). Marathon performance was associated with the magnitude and onset of decoupling, and when included in marathon performance models utilising CS and the curvature constant, prediction error was reduced from 6.45 to 5.16%.

Conclusion

Durability characteristics, assessed as internal-to-external workload ratio, show considerable inter-individual variability, and both its magnitude and onset are associated with marathon performance.

A longitudinal study on the interchangeable use of whole-body and local exercise thresholds in cycling

Purpose

This study longitudinally examined the interchangeable use of critical power (CP), the maximal lactate steady state (MLSS) and the respiratory compensation point (RCP) (i.e., whole-body thresholds), and breakpoints in muscle deoxygenation (m[HHb]_BP) and muscle activity (iEMG_BP) (i.e., local thresholds).

Methods

Twenty-one participants were tested on two timepoints (T1 and T2) with a 4-week period (study 1: 10 women, age = 27 ± 3 years, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{{2{\text{peak}}}}$$\end{document}V˙O2peak = 43.2 ± 7.3 mL min⁻¹kg⁻¹) or a 12-week period (study 2: 11 men, age = 25 ± 4 years, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{{2{\text{peak}}}}$$\end{document}V˙O2peak = 47.7 ± 5.9 mL min⁻¹ kg⁻¹) in between. The test battery included one ramp incremental test (to determine RCP, m[HHb]_BP and iEMG_BP) and a series of (sub)maximal constant load tests (to determine CP and MLSS). All thresholds were expressed as oxygen uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2) and equivalent power output (PO) for comparison.

Results

None of the thresholds were significantly different in study 1 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2: P = 0.143, PO: P = 0.281), but differences between whole-body and local thresholds were observed in study 2 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}{\text{O}}_{2}$$\end{document}V˙O2: P < 0.001, PO: P = 0.024). Whole-body thresholds showed better 4-week test–retest reliability (TEM = 88–125 mL min⁻¹ or 6–10 W, ICC = 0.94–0.98) compared to local thresholds (TEM = 189–195 mL min⁻¹ or 15–18 W, ICC = 0.58–0.89). All five thresholds were strongly associated at T1 and T2 (r = 0.75–0.99), but their changes from T1 to T2 were mostly uncorrelated (r = − 0.41–0.83).

Conclusion

Whole-body thresholds (CP/MLSS/RCP) showed a close and consistent coherence taking into account a 3–6%-bandwidth of typical variation. In contrast, local thresholds (m[HHb]_BP/iEMG_BP) were characterized by higher variability and did not consistently coincide with the whole-body thresholds. In addition, we found that most thresholds evolved independently of each other over time. Together, these results do not justify the interchangeable use of whole-body and local exercise thresholds in practice.

A critical review of critical power

The elegant concept of a hyperbolic relationship between power, velocity, or torque and time to exhaustion has rightfully captivated the imagination and inspired extensive research for over half a century. Theoretically, the relationship's asymptote along the time axis (critical power, velocity, or torque) indicates the exercise intensity that could be maintained for extended durations, or the "heavy-severe exercise boundary". Much more than a critical mass of the extensive accumulated evidence, however, has persistently shown the determined intensity of critical power and its variants as being too high to maintain for extended periods. The extensive scientific research devoted to the topic has almost exclusively centered around its relationships with various endurance parameters and performances, as well as the identification of procedural problems and how to mitigate them. The prevalent underlying premise has been that the observed discrepancies are mainly due to experimental 'noise' and procedural inconsistencies. Consequently, little or no effort has been directed at other perspectives such as trying to elucidate physiological reasons that possibly underly and account for those discrepancies. This review, therefore, will attempt to offer a new such perspective and point out the discrepancies' likely root causes.

Power profiling and the power-duration relationship in cycling: a narrative review

Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P(t) = W'/t + CP (W'-work capacity above CP; t-time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist's power profile.

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.

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

The critical power (CP) and maximal lactate steady state (MLSS) are operational surrogates of the maximal metabolic steady state (MMSS). However, their concordance and their agreement with MMSS remains variable likely due to methodological factors.

PURPOSE

To compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS.

METHODS

After a ramp-test, ten 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 3-parameter hyperbolic (CP3-hyp), 2-parameter hyperbolic (CP2-hyp), linear (CPlin), and inverse of time (CP1/Tlim) models. In addition, the model with 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 (MLSS10-30), 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[Combining Dot Above]O2 (MV[Combining Dot Above]O2ss).

RESULTS

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[Combining Dot Above]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[Combining Dot Above]O2ss. Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV[Combining Dot Above]O2ss.

CONCLUSIONS

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.

Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners

Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies-a mix of original research plus some recent results-to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.

The ramp and all-out exercise test to determine critical power: validity and robustness to manipulations in body position

Purpose

The purpose of the present study was to determine whether a contiguous ramp and all-out exercise test could accurately determine critical power (CP) in a single laboratory visit during both upright and supine cycle exercise.

Methods

Healthy males completed maximal ramp-incremental exercise on a cycle ergometer in the upright (n = 15) and supine positions (n = 8), with task failure immediately followed by a 3-min all-out phase for determination of end-test power (EP). On separate days, participants undertook four constant-power tests in either the upright or supine positions with the limit of tolerance ranging from ~ 2 to 15 min for determination of CP.

Results

During upright exercise, EP was highly correlated with (R² = 0.93, P < 0.001) and not different from CP (CP = 221 ± 40 W vs. EP = 226 ± 46 W, P = 0.085, 95% limits of agreement − 30, 19 W). During supine exercise, EP was also highly correlated with (R² = 0.94, P < 0.001) and not different from CP (CP = 140 ± 42 W vs. EP = 136 ± 40 W, P = 0.293, 95% limits of agreement − 16, 24 W).

Conclusion

The present data suggest that EP derived from a contiguous ramp all-out exercise test is not different from the gold-standard method of CP determination during both upright and supine cycle exercise when assessed at the group level. However, the wide limits of agreement observed within the present study suggest that EP and CP should not be used interchangeably.

Biological and methodological factors affecting V ̇ O 2 max response variability to endurance training and the influence of exercise intensity prescription

NEW FINDINGS

What is the topic of this review? Biological and methodological factors associated with the variable changes in cardiorespiratory fitness in response to endurance training. What advances does it highlight? Several biological and methodological factors exist that each contribute, to a given extent, to response variability. Notably, prescribing exercise intensity relative to physiological thresholds reportedly increases cardiorespiratory fitness response rates compared to when prescribed relative to maximum physiological values. As threshold-based approaches elicit more homogeneous acute physiological responses among individuals, when repeated over time, these uniform responses may manifest as more homogeneous chronic adaptations thereby reducing response variability.

ABSTRACT

Changes in cardiorespiratory fitness (CRF) in response to endurance training (ET) exhibit large variations, possibly due to a multitude of biological and methodological factors. It is acknowledged that ∼20% of individuals may not achieve meaningful increases in CRF in response to ET. Genetics, the most potent biological contributor, has been shown to explain ∼50% of response variability, whilst age, sex and baseline CRF appear to explain a smaller proportion. Methodological factors represent the characteristics of the ET itself, including the type, volume and intensity of exercise, as well as the method used to prescribe and control exercise intensity. Notably, methodological factors are modifiable and, upon manipulation, alter response rates to ET, eliciting increases in CRF regardless of an individual's biological predisposition. Particularly, prescribing exercise intensity relative to a physiological threshold (e.g., ventilatory threshold) is shown to increase CRF response rates compared to when intensity is anchored relative to a maximum physiological value (e.g., maximum heart rate). It is, however, uncertain whether the increased response rates are primarily attributable to reduced response variability, greater mean changes in CRF or both. Future research is warranted to elucidate whether more homogeneous chronic adaptations manifest over time among individuals, as a result of exposure to more homogeneous exercise stimuli elicited by threshold-based practices.

Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners

INTRODUCTION

Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons.

METHODS

We investigated running activities logged into an online fitness platform by >25,000 recreational athletes before big-city marathons. Each activity contained time, distance, and elevation every 100 m. We computed grade-adjusted pacing and the fastest pace recorded for a set of target distances (400, 800, 1000, 1500, 3000, and 5000 m). CS was determined as the slope of the distance-time relationship using all combinations of, at least, three target distances.

RESULTS

The relationship between distance and time was linear, irrespective of the target distances used (pooled mean ± SD: R = 0.9999 ± 0.0001). The estimated values of CS from all models were not different (3.74 ± 0.08 m·s), and all models correlated with marathon performance (R = 0.672 ± 0.036, error = 8.01% ± 0.51%). CS from the model including 400, 800, and 5000 m best predicted performance (R = 0.695, error = 7.67%) and was used in further analysis. Runners completed the marathon at 84.8% ± 13.6% CS, with faster runners competing at speeds closer to CS (93.0% CS for 150 min marathon times vs 78.9% CS for 360 min marathon times). Runners who completed the first half of the marathon at >94% of their CS, and particularly faster than CS, were more likely to slowdown by more than 25% in the second half of race.

CONCLUSION

This study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.

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.

Critical Speed throughout Aging: Insight into the World Masters Championships

PURPOSE

This study aimed to determine how the speed-distance relationship, described by critical speed (CS) and distance prime (D'), is altered with aging.

METHODS

Official race data from the past eight World Masters Athletics Indoor Track and Field World Championships were used for this study. CS and D' were calculated for female and male athletes (35-90 yr of age) who registered times for the 800-, 1500-, and 3000-m runs during a single championship to determine the relationship between age and CS and D'. Twenty-six athletes completed sufficient races in multiple championships to retrospectively assess the change in CS and D' over time.

RESULTS

Cross-sectional data indicated that CS continuously decreases after age 35 yr in a curvilinear manner with advancing age (R2 = 0.73, P < 0.001, n = 187), with even greater decreases in CS occurring after ~70 yr of age. D' also changed in a curvilinear manner with age (R2 = 0.45, P < 0.001, n = 103), such that decreases were observed between 35 and 70 yr, followed by an increase in D' thereafter. Retrospective, longitudinal data, with an average follow-up of 6.38 ± 1.73 yr, support these findings, indicating that the annual decrease in CS grows with advancing age (e.g., ~1% vs ~3% annual decrease in CS at age 55 vs 80 yr, respectively) and that D' shifts from an annual decrease (e.g., ~2.5% annual decrease at 55 yr) to an annual increase (e.g., ~2.5% annual increase at 80 yr) around 70 yr of age. Importantly, the relationship between CS and race pace was unaffected by age, supporting the relevance of CS throughout aging.

CONCLUSION

Even among world-class athletes, CS decreases and D' changes with aging. These adaptations may contribute to the diminished exercise ability associated with aging.

Determination of the speed-time relationship during handcycling in spinal cord injured athletes

This study aimed to determine the critical speed (CS) and the work above CS (D') from three mathematical models of para-athletes during a treadmill handcycling exercise. Nine hand-cyclists with spinal cord injuries performed a maximal incremental handcycling test and three tests to exhaustion at a constant speed to determine the speed-time relationship. The three tests to exhaustion were performed at intensities between 90% and 105% of peak speed derived from the incremental test. Then, the determination of CS and D' was modelled by linear and hyperbolic models. CS and D' did not present any significant differences among the three mathematical models. Low values in the standard error of estimate for CS were found for the three models (Linear: Distance-time: 1.7 ± 0.5%; Linear: Speed-1/time: 3.0 ± 1.9% and Hyperbolic: 1.2 ± 0.6%). Based on the simplicity to calculate, the CS modelled by linear-distance-time can be a practical method for handcyclist coaches.

A non-linear analysis of running in the heavy and severe intensity domains

PURPOSE

Altered movement complexity, indicative of system dysfunction, has been demonstrated with increased running velocity and neuromuscular fatigue. The critical velocity (CV) denotes a metabolic and neuromuscular fatigue threshold. It remains unclear whether changes to complexity during running are coupled with the exercise intensity domain in which it is performed. The purpose of this study was to examine whether movement variability and complexity differ exclusively above the CV intensity during running.

METHODS

Ten endurance-trained participants ran at 95%, 100%, 105% and 115% CV for 20 min or to task failure, whichever occurred first. Movement at the hip, knee, and ankle were sampled throughout using 3D motion analysis. Complexity of kinematics in the first and last 30 s were quantified using sample entropy (SampEn) and detrended fluctuation analysis (DFA-α). Variability was determined using standard deviation (SD).

RESULTS

SampEn decreased during all trials in knee flexion/extension and it increased in hip internal/external rotation, whilst DFA-α increased in knee internal/external rotation. SD of ankle plantar/dorsiflexion and inversion/eversion, knee internal/external rotation, and hip flexion/extension and abduction/adduction increased during trials. Hip flexion/extension SampEn values were lowest below CV. DFA-α was lower at higher velocities compared to velocities below CV in ankle plantar/dorsiflexion, hip flexion/extension, hip adduction/abduction, hip internal/external rotation. In hip flexion/extension SD was highest at 115% CV.

CONCLUSIONS

Changes to kinematic complexity over time are consistent between heavy and severe intensity domains. The findings suggest running above CV results in increased movement complexity and variability, particularly at the hip, during treadmill running.

Agreement of maximal lactate steady state with critical power and physiological thresholds in rowing

The aim of this study was threefold: (a) to compare the maximal lactate steady state (MLSS) with critical power (CP); (b) to describe the relationship of MLSS with rowing performances; and (c) to verify the agreement of MLSS with several exercise intensity thresholds in rowers. Fourteen male rowers (mean [SD]: age = 26 [13] years; height = 1.82 [0.05] m; body mass = 81.0 [7.6] kg) performed on a rowing ergometer: (I) discontinuous incremental test with 3 min stages and 30-s recovery intervals (INC_3min); (II) continuous incremental test with 60-s stages (INC_1min); (III) two to four constant workload tests to determine MLSS; and (IV) performance tests of 500, 1000, 2000 and 6000 m to determine CP. Twenty-seven exercise intensity thresholds based on blood lactate, heart rate and ventilatory responses were determined by incremental tests, and then compared with MLSS. CP (257 [38] W) was higher than MLSS (187 [25] W; p < 0.001), with a very large mean difference (37%), large typical error of estimate (14%) and moderate correlation (r = 0.48). Despite the correlations between MLSS and most intensity thresholds (r > 0.70), all presented low correspondence (TEE > 5%), with a lower bias found between MLSS and the first intensity thresholds (-12.5% to 4.1%). MLSS was correlated with mean power during 500 m (r = 0.65), 1000 m (r = 0.86) and 2000 m (r = 0.78). In conclusion, MLSS intensity is substantially lower than CP and presented low agreement with 27 incremental-derived thresholds, questioning their use to estimate MLSS during rowing ergometer exercise.Highlights MLSS was substantially lower than CP in rowing exercise with a mean difference of 37%, much larger than the difference commonly found in running and cycling exercise (i.e., ?10%).A clear disagreement was reported between MLSS and 27 physiological thresholds determined in different incremental tests.There is a positive association of MLSS with 500, 1000 and 2000 m rowing ergometer performance tests.