A Novel Approach to Determining the Alactic Time Span in Connection with Assessment of the Maximal Rate of Lactate Accumulation in Elite Track Cyclists

The Influence of Pedaling Frequency on Blood Lactate Accumulation in Cycling Sprints

Anaerobic performance diagnostics in athletes relies on accurate measurements of blood lactate concentration and the calculation of blood lactate accumulation resulting from glycolytic processes. In this study, we investigated the impact of pedaling frequency on blood lactate accumulation during 10-second maximal isokinetic cycling sprints. Thirteen trained males completed five 10-second maximal isokinetic cycling sprints on a bicycle ergometer at different pedaling frequencies (90 rpm, 110 rpm, 130 rpm, 150 rpm, 170 rpm) with continuous power and frequency measurement. Capillary blood samples were taken pre-exercise and up to 30 minutes post-exercise to determine the maximum blood lactate concentration.Blood lactate accumulation was calculated as the difference between maximal post-exercise and pre-start blood lactate concentration. Repeated measurement ANOVA with Bonferroni-adjusted post hoc t-tests revealed significant progressive increases in maximal blood lactate concentration and accumulation with higher pedaling frequencies (p<0.001; η2+>+0.782).The findings demonstrate a significant influence of pedaling frequency on lactate accumulation, emphasizing its relevance in anaerobic diagnostics. Optimal assessment of maximal lactate formation rate is suggested to require a pedaling frequency of at least 130 rpm or higher, while determining metabolic thresholds using the maximal lactate formation rate may benefit from a slightly lower pedaling frequency.

Reliability of power output, maximal rate of capillary blood lactate accumulation, and phosphagen contribution time following 15-s sprint cycling in amateur cyclists

Based on Mader's mathematical model, the rate of capillary blood lactate concentration (νLamax) following intense exercise is thought to reflect the maximal glycolytic rate. We aimed to investigate the reliability of important variables of Mader's model (i.e. power output, lactate accumulation, predominant phosphagen contribution time frames (tP Cr)) and resulting νLamax values derived during and after a 15-s cycling sprint. Fifty cyclists performed a 15-s all-out sprint test on a Cyclus2 ergometer three times. The first sprint test was considered a familiarization trial. Capillary blood was sampled before and every minute (for 8 min) after the sprint to determine νLamax. Test-retest analysis between T2 and T3 revealed excellent reliability for power output (Pmean and Ppeak; ICC = 0.99, 0.99), ∆La and νLamax with tPCr of 3.5 s (ICC = 0.91, 0.91). νLamax calculated with tPCr = tP peak (ICC = 0.87) and tP Cr = tPpeak-3.5% (ICC = 0.79) revealed good reliability. tPpeak and tPpeak-3.5% revealed only poor and moderate reliability (ICC = 0.41, 0.52). Power output and ∆La are reliable parameters in the context of this test. Depending on tPCr, reliability of νLamax varies considerably with tP Cr of 3.5 s showing excellent reliability. We recommend standardization of this type of testing especially tP Cr.

A Novel Approach to the Determination of Time- and Fatigue-Dependent Efficiency during Maximal Cycling Sprints

BACKGROUND

During maximal cycling sprints, efficiency (η) is determined by the fiber composition of the muscles activated and cadence-dependent power output. To date, due to methodological limitations, it has only been possible to calculate gross efficiency (i.e., the ratio of total mechanical to total metabolic work) in vivo without assessing the impact of cadence and changes during exercise. Eliminating the impact of cadence provides optimal efficiency (η_opt), which can be modeled as a function of time. Here, we explain this concept, demonstrate its calculation, and compare the values obtained to actual data. Furthermore, we hypothesize that the time course of maximal power output (P_max) reflects time-dependent changes in η_opt.

METHODS

Twelve elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) and a maximal-pedaling test on a cycle ergometer. Crank force and cadence were monitored continuously to determine fatigue-free force-velocity profiles (F/v) and fatigue-induced changes in P_max. Respiratory gases were measured during and for 30 min post-exercise. Prior to and following each sprint, lactate in capillary blood was determined to calculate net blood lactate accumulation (ΔBLC). Lactic and alactic energy production were estimated from ΔBLC and the fast component of excess post-exercise oxygen consumption. Aerobic energy production was determined from oxygen uptake during exercise. Metabolic power (MP) was derived from total metabolic energy (W_TOT). η_opt was calculated as P_max divided by MP. Temporal changes in P_max, W_TOT, and η_opt were analyzed by non-linear regression.

RESULTS

All models showed excellent quality (R² > 0.982) and allowed accurate recalculation of time-specific power output and gross efficiency (R² > 0.986). The time-constant for P_max(t) (τ_P) was closely correlated with that of η_opt (τ_η; r = 0.998, p < 0.001). Estimating efficiency using τ_P for τ_η led to a 0.88 ± 0.35% error.

CONCLUSIONS

Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by η_opt(t) using a mono-exponential equation whose time constant can be estimated from P_max(t).