On-field prediction vs monitoring of aerobic capacity markers using submaximal lactate and heart rate measures.

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-1 (BLC10 ) and the speed associated with 90% of maximal heart rate (S90 ) 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 BLC10 and S90 with running speeds at FBLC of 3 (S3mM) and 4 mmol·L-1 (S4mM) in 100 athletes. Study II: A cross-validation study assessing the predictive capacity of BLC10 and S90 to estimate FBLC thresholds in real practice. Study III: A longitudinal study examining whether training-induced changes in FBLC thresholds could be monitored using BLC10 and S90 in 80 athletes tested before and after an intensified training period. Study I: BLC10 (r=-.87 to -.89) and S90 (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: BLC10 was very largely related to training-induced changes in FBLC thresholds (r=-.72 to -.76; P<.001). Increases in S90 were associated with improvements in FBLC thresholds, but decreases in S90 led to unclear changes in FBLC thresholds. This study supports the use of BLC10 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 S90 to detect declines in endurance performance.

Estimation of the Maximal Lactate Steady State in Endurance Runners.

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); R(2)=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·ΔLa30-6); R(2)=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.

Estimation of the Maximal Lactate Steady State in Junior Soccer Players.

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.