This report aims to generate an evidence-based debate of the Critical Power (CP), or its analogous Critical Speed (CS), concept. Race times of top Spanish runners were utilized to calculate CS based on three (1500-m to 5000-m; CS 1.5-5km ) and four (1500-m to 10000-m; CS 1.5-10km ) distance performances. Male running world records from 1000 to 5000-m (CS 1-5km ), 1000 to 10,000-m (CS 1-10km ), 1000-m to half marathon (CS 1km-half marathon ), and 1000-m to marathon (CS 1km-marathon ) distance races were also utilized for CS calculations. CS 1.5-5km (19.62 km h −1 ) and CS 1.5-10km (18.68 km h −1 ) were different (p < 0.01), but both approached the average race speed of the longest distance chosen in the model, and were remarkably homogeneous among subjects (97% ±1% and 98% ±1%, respectively). Similar results were obtained using the world records. CS values progressively declined, until reaching a CS 1km-marathon value of 20.77 km h −1 (10% lower than CS 1-5km ).Each CS value approached the average speed of the longest distance chosen in the model (96.4%-99.8%). A power function better fitted the speed-time relationship compared with the standardized hyperbolic function. However, the horizontal asymptote of a power function is zero. This better approaches the classical definition of CP: the power output that can be maintained almost indefinitely without exhaustion. Beyond any sophisticated mathematical calculation, CS corresponds to 95%-99% of the average speed of the longest distance chosen as an exercise trial. CP could be considered a mathematical artifact rather than an important endurance performance marker. In such a case, the consideration of CP as a physiological "gold-standard" should be reevaluated.

This study aimed to predict the velocity corresponding to the maximal lactate steady state (MLSS(V)) from non-invasive variables obtained during an incremental maximal running test (University of Montreal Track Test, UMTT) and to determine whether a single constant velocity test (CVT), performed several days after the UMTT, could estimate the MLSS(V). During a period of 3 weeks, 20 male junior soccer players performed: (1) a UMTT, and (2) several 20-min CVTs to determine MLSS(V) to a precision of 0.35 km·h(-1). Maximal aerobic velocity (MAV) and velocity at 80% of maximum heart rate (V80%HRmax) were strong predictors of MLSS(V). A regression equation was obtained: MLSS(V)=(1.106·MAV) - (0.309·V(80%HRmax)) - 3.024; R2=0.60. Running velocity during CVT (V(CVT)) and blood lactate at 10 (La10) and 20 (La20) minutes further improved the MLSS(V) prediction: MLSS(V)=V(CVT)+0.26 - (0.812·ΔLa(20-10)); R2=0.66. MLSS(V) can be estimated from MAV and V(80%HRmax) during a single incremental maximal running test among a homogeneous group of soccer players. This estimation can be improved by performing an additional CVT. In terms of accuracy, simplicity and cost-effectiveness, the reported regression equations can be used for the assessment and training prescription of endurance in team sport players.

The aim of this study was to investigate whether the speed associated with 90% of maximal heart rate (S90%HRmax) could predict speeds at fixed blood lactate concentrations of 3 mmol·L(-1) (S3mM) and 4 mmol·L(-1) (S4mM). Professional team-sport players of futsal (n = 10), handball (n = 16), and basketball (n = 10) performed a 4-stage discontinuous progressive running test followed, if exhaustion was not previously achieved, by an additional maximal continuous incremental running test to attain maximal heart rate (HRmax). The individual S3mM, S4mM, and S90%HRmax were determined by linear interpolation. S3mM (11.6 ± 1.5 km·h(-1)) and S4mM (12.5 ± 1.4 km·h(-1)) did not differ (p > 0.05) from S90%HRmax (12.0 ± 1.2 km·h(-1)). Very large significant (p < 0.001) relationships were found between S90%HRmax and S3mM (r = 0.82; standard error of the estimates [SEE] = 0.87 km·h(-1)), as well as between S90%HRmax and S4mM (r = 0.82; SEE = 0.87 km·h(-1)). S3mM and S4mM inversely correlated with %HRmax associated with running speeds of 10 and 12 km·h(-1) (r = 0.78-0.81; p < 0.001; SEE = 0.94-0.87 km·h(-1)). In conclusion, S3mM and S4mM can be accurately predicted by S90%HRmax in professional team-sport players.

This study aimed to predict the velocity corresponding to the maximal lactate steady state (MLSSV) from non-invasive variables obtained during a maximal multistage running field test (modified University of Montreal Track Test, UMTT), and to determine whether a single constant velocity test (CVT), performed several days after the UMTT, could estimate the MLSSV. Within 4?5 weeks, 20 male runners performed: 1) a modified UMTT, and 2) several 30?min CVTs to determine MLSSV to a precision of 0.25?km?h?1. Maximal aerobic velocity (MAV) was the best predictor of MLSSV. A regression equation was obtained: MLSSV=1.425+(0.756?MAV); R2=0.63. Running velocity during the CVT (VCVT) and blood lactate at 6 (La6) and 30 (La30) min further improved the MLSSV prediction: MLSSV=VCVT+0.503 ? (0.266??La30?6); R2=0.66. MLSSV can be estimated from MAV during a single maximal multistage running field test among a homogeneous group of trained runners. This estimation can be further improved by performing an additional CVT. In terms of accuracy, simplicity and cost-effectiveness, the reported regression equations can be used for the assessment and training prescription of endurance runners.

This study aimed to validate the use of a single blood lactate concentration measure taken following a 12 km h running stage (BLC) to predict and monitor fixed blood lactate concentration (FBLC) thresholds. Three complementary studies were undertaken. Study I: the relationships between BLC and the running speeds at FBLC of 3 mmol L (S3mM) and 4 mmol L (S4mM) measured during a multistage running field test were examined in 136 elite athletes. Study II: data from 30 athletes tested one year apart were used to test the predictive capacity of the equations obtained in Study I. Study III: 80 athletes were tested before and after an intensified training period to examine whether training-induced changes in FBLC thresholds could be predicted and monitored by BLC. Study I: BLC was significantly (P < 0.001) and inversely related to S3mM (R = 0.89) and S4mM (R = 0.95). Study II: prediction models yielded robust correlations between the estimated and measured FBLC thresholds (r = 0.94-0.99; P < 0.001). Study III: estimated changes predicted actual training-induced changes in FBLC thresholds (r = 0.81-0.91; P < 0.001). This study gives empirical support to use a single lactate measure during a sub-maximal running field test as a simple, low-cost and practical alternative to FBLC thresholds in athletes.

No abstract

A pesar de que las acciones físicas que los jugadores de baloncesto en silla de ruedas (BSR) deben realizar en los partidos son acciones que implican múltiples cambios de dirección y acciones de esprint y cambio de dirección repetidos, en la actualidad no existe ningún trabajo científico que analice la reproducibilidad de distintos test de capacidad de cambio de dirección, esprines y cambios de dirección repetidos. Diecisiete jugadores (15 hombres y 2 mujeres; 25,9±9,7 años) pertenecientes a un equipo de BSR de Primera División Española participaron en este estudio. En la primera semana (Test), en dos sesiones distintas, se realizaron 5 test [Test 3-3-6, Test 505, Test Illinois, Test de Esprines Repetidos (RSA) y el test repetido Modified Agility Test (rMAT)], y una semana después se volvieron a repetir (re-Test). Los resultados de los test de capacidad de cambio de dirección mostraron altos valores de reproducibilidad (CCI>0,74; CV<3,82±2,62%; SEM<0,33). En cuanto al RSA, la reproducibilidad tanto en la media de las repeticiones como del mejor intento fue alta (CCI>0,90; CV<3,85±3,59%; SEM<0,04). Con respecto al rMAT, la media y la mejor repetición mostraron también una reproducibilidad alta (CCI>0,94; CV<2,18±1,73%; SEM=0,27). Sin embargo, el índice de fatiga (Sdec) no mostró buenos valores de reproducibilidad ni en el RSA ni en el rMAT. Todos los test presentaron altos valores de reproducibilidad, por lo que podrían ser utilizados por los entrenadores y preparadores físicos como herramienta para evaluar la evolución de la capacidad física en jugadores de BSR.

This study aimed to validate the use of a single blood lactate concentration measurement taken following a 5-minute running bout at 10 km·h (BLC ) and the speed associated with 90% of maximal heart rate (S ) to predict and monitor fixed blood lactate concentration (FBLC) thresholds in athletes. Three complementary studies were undertaken. Study I: A cross-sectional study examining the associations of BLC and S with running speeds at FBLC of 3 (S3mM) and 4 mmol·L (S4mM) in 100 athletes. Study II: A cross-validation study assessing the predictive capacity of BLC and S to estimate FBLC thresholds in real practice. Study III: A longitudinal study examining whether training-induced changes in FBLC thresholds could be monitored using BLC and S in 80 athletes tested before and after an intensified training period. Study I: BLC (r=-.87 to -.89) and S (r=.73-.79) were very largely (P<.001) related to FBLC thresholds. Study II: Predictive models yielded robust correlations between estimated and measured FBLC thresholds (r=.75-.91; P<.001). The limits of agreements, however, revealed that prediction of FBLC thresholds could be biased up to 9%-15%. Study III: BLC was very largely related to training-induced changes in FBLC thresholds (r=-.72 to -.76; P<.001). Increases in S were associated with improvements in FBLC thresholds, but decreases in S led to unclear changes in FBLC thresholds. This study supports the use of BLC as a simple, low-cost, non-fatiguing, and time-efficient functional variable to monitor, but not predict, FBLC thresholds in athletes. The present results also question the use of S to detect declines in endurance performance.