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.

Understanding optimal cadence dynamics: a systematic analysis of the power-velocity relationship in track cyclists with increasing exercise intensity.

Background: This study aimed to investigate the changes in force-velocity (F/v) and power-velocity (P/v) relationships with increasing work rate up to maximal oxygen uptake and to assess the resulting alterations in optimal cadence, particularly at characteristic metabolic states. Methods: Fourteen professional track cyclists (9 sprinters, 5 endurance athletes) performed submaximal incremental tests, high-intensity cycling trials, and maximal sprints at varied cadences (60, 90, 120 rpm) on an SRM bicycle ergometer. Linear and non-linear regression analyses were used to assess the relationship between heart rate, oxygen uptake (V.O2), blood lactate concentration and power output at each pedaling rate. Work rates linked to various cardiopulmonary and metabolic states, including lactate threshold (LT1), maximal fat combustion (FATmax), maximal lactate steady-state (MLSS) and maximal oxygen uptake (V.O2max), were determined using cadence-specific inverse functions. These data were used to calculate state-specific force-velocity (F/v) and power-velocity (P/v) profiles, from which state-specific optimal cadences were derived. Additionally, fatigue-free profiles were generated from sprint data to illustrate the entire F/v and P/v continuum. Results: HR, V.O2 demonstrated linear relationships, while BLC exhibited an exponential relationship with work rate, influenced by cadence (p < 0.05, η2 ≥ 0.655). Optimal cadence increased sigmoidally across all parameters, ranging from 66.18 ± 3.00 rpm at LT1, 76.01 ± 3.36 rpm at FATmax, 82.24 ± 2.59 rpm at MLSS, culminating at 84.49 ± 2.66 rpm at V.O2max (p < 0.01, η2 = 0.936). A fatigue-free optimal cadence of 135 ± 11 rpm was identified. Sprinters and endurance athletes showed no differences in optimal cadences, except for the fatigue-free optimum (p < 0.001, d = 2.215). Conclusion: Optimal cadence increases sigmoidally with exercise intensity up to maximal aerobic power, irrespective of the athlete's physical condition or discipline. Threshold-specific changes in optimal cadence suggest a shift in muscle fiber type recruitment toward faster types beyond these thresholds. Moreover, the results indicate the need to integrate movement velocity into Henneman's hierarchical size principle and the critical power curve. Consequently, intensity zones should be presented as a function of movement velocity rather than in absolute terms.

Applications of near-infrared spectroscopy in "anaerobic" diagnostics - SmO2 kinetics reflect PCr dephosphorylation and correlate with maximal lactate accumulation and maximal pedalling rate.

We investigated the relationship of the time-dependent behaviour of muscle oxygen saturation SmO2(t), phosphagen energy supply WPCr(t) and blood lactate accumulation ΔBLC(t) during a 60-s all-out cycling sprint and tested SmO2(t) for correlations with the end of the fatigue-free state tFf, maximal pedalling rate PRmax and maximal blood lactate accumulation rate v̇Lamax. Nine male elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) on a cycle ergometer. Crank force and cadence were monitored continuously to determine PRmax and tFf based on force-velocity profiles. SmO2 of the vastus lateralis muscle and respiratory gases were measured until the 30th minute after exercise. WPCr was calculated based on the fast component of the post-exercise oxygen uptake for each sprint. Before and for 30 minutes after each sprint, capillary blood samples were taken to determine the associated ΔBLC. Temporal changes of SmO2, WPCr and ΔBLC were analysed via non-linear regression analysis. v̇Lamax was calculated based on ΔBLC(t) as the highest blood lactate accumulation rate. All models showed excellent quality (R2 > 0.95). The time constant of SmO2(t) τSmO2 = 2.93 ± 0.65 s was correlated with the time constant of WPCr(t) τPCr = 3.23 ± 0.67 s (r = 0.790, p < 0.012), v̇Lamax = 0.95 ± 0.18 mmol · l-1 · s-1 (r = 0.768, p < 0.017) and PRmax = 299.51 ± 14.70 rpm (r = -0.670, p < 0.049). tFf was correlated with τSmO2 (r = 0.885, p < 0.001). Our results show a time-dependent reflection of SmO2 kinetics and phosphagen energy contribution during a 60-s maximal cycling sprint. A high v̇Lamax results in a reduction, a high PRmax in an increase of the desaturation rate. The half-life of SmO2 desaturation indicates the end of the fatigue-free state.

Track cycling sprint sex differences using power data.

Objectives: Currently, there are no data on sex differences in the power profiles in sprint track cycling. This cross-section study analyses retrospective data of female and male track sprint cyclists for sex differences. We hypothesized that women would exhibit lower peak power to weight than men, as well as demonstrate a different distribution of power durations related to sprint cycling performance. Design: We used training, testing, and racing data from a publicly available online depository (www.strava.com), for 29 track sprint cyclists (eight women providing 18 datasets, and 21 men providing 54 datasets) to create sex-specific profiles. R2 was used to describe model quality, and regression indices are used to compare watts per kilogram (W/kg) for each duration for both sexes against a 1:1 relationship expected for 15-s:15-s W/kg. Results: We confirmed our sample were sprint cyclists, displaying higher peak and competition power than track endurance cyclists. All power profiles showed a high model quality (R2 ≥ 0.77). Regression indices for both sexes were similar for all durations, suggesting similar peak power and similar relationship between peak power and endurance level for both men and women (rejecting our hypothesis). The value of R2 for the female sprinters showed greater variation suggesting greater differences within female sprint cyclists. Conclusion: The main finding shows female sprint cyclists in this study have very similar relationships between peak power and endurance power as men. Higher variation in W/kg for women in this study than men, within these strong relationships, indicates women in this study, had greater inter-athlete variability, and may thus require more personalised training. Future work needs to be performed with larger samples, and at different levels to optimize these recommendations.

Power-duration relationship comparison in competition sprint cyclists from 1-s to 20-min. Sprint performance is more than just peak power.

Current convention place peak power as the main determinant of sprint cycling performance. This study challenges that notion and compares two common durations of sprint cycling performance with not only peak power, but power out to 20-min. There is also a belief where maximal efforts of longer durations will be detrimental to sprint cycling performance. 56 data sets from 27 cyclists (21 male, 6 female) provided maximal power for durations from 1-s to 20-min. Peak power values are compared to assess the strength of correlation (R2), and any relationship (slope) across every level. R2 between 15-s- 30-s power and durations from 1-s to 20-min remained high (R2 ≥ 0.83). Despite current assumptions around 1-s power, our data shows this relationship is stronger around competition durations, and 1-s power also still shared strong relationships with longer durations out to 20-min. Slopes for relationships at shorter durations were closer to a 1:1 relationship than longer durations, but closer to long-duration slopes than to a 1:1 line. The present analyses contradicts both well-accepted hypotheses that peak power is the main driver of sprint cycling performance and that maximal efforts of longer durations out to 20-min will hinder sprint cycling. This study shows the importance and potential of training durations from 1-s to 20-min over a preparation period to improve competition sprint cycling performance.

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 (ηopt), 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 (Pmax) reflects time-dependent changes in ηopt. 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 Pmax. 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 (WTOT). ηopt was calculated as Pmax divided by MP. Temporal changes in Pmax, WTOT, and ηopt 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 Pmax(t) (τP) was closely correlated with that of ηopt (τη; r = 0.998, p < 0.001). Estimating efficiency using τP for τη led to a 0.88 ± 0.35% error. CONCLUSIONS: Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by ηopt(t) using a mono-exponential equation whose time constant can be estimated from Pmax(t).

The Concept of Optimal Dynamic Pedalling Rate and Its Application to Power Output and Fatigue in Track Cycling Sprinters-A Case Study.

Sprint races in track cycling are characterised by maximal power requirements and high-power output over 15 to 75 s. As competition rules limit the athlete to a single gear, the choice of gear ratio has considerable impact on performance. Traditionally, a gear favouring short start times and rapid acceleration, i.e., lower transmission ratios, was chosen. In recent years, track cyclists tended to choose higher gear ratios instead. Based on a review of the relevant literature, we aimed to provide an explanation for that increase in the gear ratio chosen and apply this to a 1000 m time trial. Race data with continuous measurements of crank force and velocity of an elite track cyclist were analysed retrospectively regarding the influence of the selected gear on power, cadence and resulting speed. For this purpose, time-dependent maximal force-velocity (F/v) profiles were used to describe changes in performance with increasing fatigue. By applying these profiles to a physical model of track cycling, theoretical power output, cadence and resulting speed were calculated for different scenarios. Based on previous research results, we assume a systematic and predictable decline in optimal cadence with increasing fatigue. The choice of higher gear ratios seems to be explained physiologically by the successive reduction in optimal cadence as fatigue sets in. Our approach indicates that average power output can be significantly increased by selecting a gear ratio that minimises the difference between the realised cadence and the time-dependent dynamic optimum. In view of the additional effects of the gear selection on acceleration and speed, gear selection should optimally meet the various requirements of the respective sprint event.

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

PURPOSE: Following short-term all-out exercise, the maximal rate of glycolysis is frequently assessed on the basis of the maximal rate of lactate accumulation in the blood. Since the end of the interval without significant accumulation (talac) is 1 of 2 denominators in the calculation employed, accurate determination of this parameter is crucial. Although the very existence and definition of talac, as well as the validity of its determination as time-to-peak power (tPpeak), remain controversial, this parameter plays a key role in anaerobic diagnostics. Here, we describe a novel approach to determination of talac and compare it to the current standard. METHODS: Twelve elite track cyclists performed 3 maximal sprints (3, 8, and 12 s) and a high-rate, low-resistance pedaling test on an ergometer with monitoring of crank force and pedaling rate. Before and after each sprint, capillary blood samples were taken for determination of lactate accumulation. Fatigue-free force-velocity and power-velocity profiles were generated. talac was determined as tPpeak and as the time point of the first systematic deviation from the force-velocity profile (tFf). RESULTS: Accumulation of lactate after the 3-second sprint was significant (0.58 [0.19] mmol L-1; P < .001, d = 1.982). tFf was <3 seconds and tPpeak was ≥3 seconds during all sprints (P < .001, d = - 2.111). Peak power output was lower than maximal power output (P < .001, d = -0.937). Blood lactate accumulation increased linearly with increasing duration of exercise (R2 ≥ .99) and intercepted the x-axis at ∼tFf. CONCLUSION: Definition of talac as tPpeak can lead to incorrect conclusions. We propose determination of talac based on tFf, the end of the fatigue-free state that may reflect the beginning of blood lactate accumulation.

Fatigue-Free Force-Velocity and Power-Velocity Profiles for Elite Track Sprint Cyclists: The Influence of Duration, Gear Ratio and Pedalling Rates.

Background: Maximal force-velocity (F/v) profiles for track cyclists are commonly derived from ergometer sprints using an isovelocity or isoinertial approach. Previously, an attempt was made to derive maximal F/v profiles from a single maximal 65-m sprint on the cycling track. Hypothesising that this approach may not accurately reflect the fatigue-free F/v profile, we propose an alternative procedure and compare it to the previous method. Moreover, we test for the impact of gear ratio on diagnostic results. Methods: Twelve elite track cyclists completed a high-cadence low-resistance pedalling test on a freestanding roller (motoric test) and two series of three maximal 65-m sprints on a cycling track with different gear ratios. F/v profiles were calculated based on the measured crank force and cadence either during the first 6−7 revolutions (≤6 s) on the track (model I) or were derived from the first 3−4 revolutions (≤3 s) on the track combined with 1 or 2 fatigue-free cycles at cadences above 160 rpm from the motoric test (model II). Results: Although both models exhibit high-to-excellent linearity between force and velocity, the extrapolated isometric force was higher (1507.51 ± 257.60 N and 1384.35 ± 276.84 N; p < 0.002; d = 2.555) and the slope steeper (−6.78 ± 1.17 and −5.24 ± 1.11; p < 0.003, d = −2.401) with model I. An ICC of 1.00 indicates excellent model consistency when comparing the F/v profiles (model II) derived from the different geared sprints. Conclusions: Assuring fatigue-free measurements and including high-cadence data points in the calculations provide valid maximal F/v and P/v profiles from a single acceleration-sprint independent of gear ratio.