Energy metabolism and muscle activation heterogeneity explain V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ slow component and muscle fatigue of cycling at different intensities.

NEW FINDINGS: What is the central question of this study? What are the physiological mechanisms underlying muscle fatigue and the increase in the O2 cost per unit of work during high-intensity exercise? What is the main finding and its importance? Muscle fatigue happens before, and does not explain, the V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ slow component ( V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ ), but they share the same origin. Muscle activation heterogeneity is associated with muscle fatigue and V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ . Knowing this may improve training prescriptions for healthy people leading to improved public health outcomes. ABSTRACT: This study aimed to explain the V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ slow component ( V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ ) and muscle fatigue during cycling at different intensities. The muscle fatigue of 16 participants was determined through maximal isokinetic effort lasting 3 s during constant work rate bouts of moderate (MOD), heavy (HVY) and very heavy intensity (VHI) exercise. Breath-by-breath V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ , near-infrared spectroscopy signals and EMG activity were analysed (thigh muscles). V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ was higher during VHI exercise (∼70% vs. ∼28% of V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ reserve in HVY). The deoxygenated haemoglobin final value during VHI exercise was higher than during HVY and MOD exercise (∼90% of HHb physiological normalization, vs. ∼82% HVY and ∼45% MOD). The muscle fatigue was greater after VHI exercise (∼22% vs. HVY ∼5%). There was no muscle fatigue after MOD exercise. The greatest magnitude of muscle fatigue occurred within 2 min (VHI ∼17%; HVY ∼9%), after which it stabilized. No significant relationship between V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ and muscle force production was observed. The τ of muscle V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ was significantly related (R2  = 0.47) with torque decrease for VHI. Type I and II muscle fibre recruitment mainly in the rectus femoris moderately explained the muscle fatigue (R2  = 0.30 and 0.31, respectively) and the V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ (R2  = 0.39 and 0.27, respectively). The V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ is also partially explained by blood lactate accumulation (R2  = 0.42). In conclusion muscle fatigue and O2 cost seem to share the same physiological cause linked with a decrease in the muscle V ̇ O 2 ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}}$ and a change in lactate accumulation. Muscle fatigue and V ̇ O 2 sc ${\dot{V}}_{{{\rm{O}}}_{\rm{2}}{\rm{sc}}}$ are associated with muscle activation heterogeneity and metabolism of different muscles activated during cycling.

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.

Reliability and Validity of Cycling Sprint Performance at Isolinear Mode Without Torque Factor: A Preliminary Study in Well-Trained Male Cyclists.

Purpose: This study aimed to compare the performance-derived parameters utilizing isolinear (ISOLIN) and isovelocity (ISOVEL) sprint cycling modes. Method: For that, 20 male trained cyclists performed 2 sprints of 7 s on an electromagnetically braked cycle ergometer in ISOLIN and six sprints in ISOVEL mode with cadences between 90 and 180 rpm, each separated by 3-min. A linear function modeled the sprints within each mode to extrapolate maximal cadence (CMAX) and torque (TMAX), and a quadratic function was used to extrapolate the apex defined as optimal cadence power (OPTCAD) and peak power output (PMAX). Fifteen subjects performed another 4 sprints at ISOLIN mode on different days to verify the reliability. Results: The measures from the power-cadence relationship were not different between the ISOLIN and ISOVEL modes. Although significant differences were detected in the T-C relationship, TMAX was greater at ISOLIN than ISOVEL (p = .006). On the other hand, CMAX was higher at ISOVEL than ISOLIN (p < .001). The correlation between parameters was large to very large (r = 0.51 to 0.89). However, high limits of agreement were verified. The ISOLIN presented consistency during the trials, and the random errors were acceptable (CV = 5.3% to 11.5%). Conclusion: Using the power-cadence relationship, PMAX and OPTCAD could be detected similarly between the two sprint modes (ISOLIN and ISOVEL). Thus, the findings demonstrated that a single ISOLIN sprint test could be a suitable tool for quantifying the time course of muscle fatigue during and after cycling exercises in well-trained male cyclists.

Heart rate variability kinetics during different intensity domains of cycling exercise in healthy subjects.

The purpose of this study was to verify the heart rate variability (HRV) and heart rate (HR) kinetics during the fundamental phase in different intensity domains of cycling exercise. Fourteen males performed five exercise sessions: (1) maximal incremental cycling test; (2) two rest-to-exercise transitions for each intensity domain, that is, heavy (Δ30) and severe (Δ60) domains. HRV markers (SD1 and SD2) and HR kinetics in the fundamental phase were analyzed by first-order exponential fitting. There were no significant differences in amplitude values between SD1Δ30 (8.98 ± 3.52 ms) and SD1Δ60 (9.44 ± 3.24 ms) and SD2Δ30 (24.93 ± 9.16 ms) and SD2Δ60 (25.98 ± 7.29 ms). Significant difference was observed between HRΔ30 (52 ± 7 bpm) and HRΔ60 (63 ± 8 bpm). The time constant (τ) values were significantly different between SD1Δ30 (17.61 ± 6.26 s) and SD1Δ60 (13.86 ± 5.90 s), but not between SD2Δ30 (20.06 ± 3.73 s) and SD2Δ60 (19.47 ± 6.03 s) or HRΔ30 (56.75 ± 18.22 s) and HRΔ60 (58.49 ± 15.61 s). However, the τ values for HRΔ30 were higher and significantly different in relation to SD1Δ30 and SD2Δ30, as well as for HRΔ60 in relation to SD1Δ60 and SD2Δ60. The kinetics of the autonomic variable (SD1 marker) was accelerated by the increased intensity. Moreover, significant differences were found for the τ values, with faster HRV markers than HR, in both intensities of Δ30 and Δ60, which suggests that these variables indicate distinct and specific cardiac autonomic response times during different intensity domains in cycling.HIGHLIGHTSThe application of HRV to optimize exercise prescription at different effort intensities is extremely important to obtain assertive and effective results.Analysis of the kinetic responses of HRV is a useful tool for the evaluation of exercise performance and health status.A faster kinetics was found for HRV markers in comparison to HR, for both intensities analysed, which suggests that these variables indicate distinct and specific cardiac autonomic response times during different intensity domains in cycling.

Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners.

INTRODUCTION: The purpose of this study was to evaluate the application of the Dmax method on heart rate variability (HRV) to estimate the lactate thresholds (LT), during a maximal incremental running test (MIRT). METHODS: Nineteen male runners performed two MIRTs, with the initial speed at 8 km·h-1 and increments of 1 km·h-1 every 3 minutes, until exhaustion. Measures of HRV and blood lactate concentrations were obtained, and lactate (LT1 and LT2) and HRV (HRVTDMAX1 and HRVTDMAX2) thresholds were identified. ANOVA with Scheffe's post hoc test, effect sizes (d), the bias ± 95% limits of agreement (LoA), standard error of the estimate (SEE), Pearson's (r), and intraclass correlation coefficient (ICC) were calculated to assess validity. RESULTS: No significant differences were observed between HRVTDMAX1 and LT1 when expressed for speed (12.1 ± 1.4 km·h-1 and 11.2 ± 2.1 km·h-1; p=0.55; d = 0.45; r = 0.46; bias ± LoA = 0.8 ± 3.7 km·h-1; SEE = 1.2 km·h-1 (95% CI, 0.9-1.9)). Significant differences were observed between HRVTDMAX2 and LT2 when expressed for speed (12.0 ± 1.2 km·h-1 and 14.1 ± 2.5 km·h-1; p=0.00; d = 1.21; r = 0.48; bias ± LoA = -1.0 ± 1.8 km·h-1; SEE = 1.1 km·h-1 (95% CI, 0.8-1.6)), respectively. Reproducibility values were found for the LT1 (ICC = 0.90; bias ± LoA = -0.7 ± 2.0 km·h-1), LT2 (ICC = 0.97; bias ± LoA = -0.1 ± 1.1 km·h-1), HRVTDMAX1 (ICC = 0.48; bias ± LoA = -0.2 ± 3.4 km·h-1), and HRVTDMAX2 (ICC = 0.30; bias ± LoA = 0.3 ± 3.5 km·h-1). CONCLUSIONS: The Dmax method applied over a HRV dataset allowed the identification of LT1 that is close to aerobic threshold, during a MIRT.

Caracterização da curva da frequência cardíaca durante teste incremental máximo em esteira / Characterization of the heart rate curve during a maximum incremental test on a treadmill

O propósito deste estudo foi analisar o comportamento da frequência cardíaca (FC) versus a carga de trabalho crescente (CTC) em teste de esteira, utilizando três modelos matemáticos (linear, linear com dois segmentos de reta e sigmóide) e verificar qual o melhor modelo que possibilita a identificação de um limiar de FC que pudesse servir de preditor para os limiares ventilatórios (LV1 e LV2). Vinte e dois homens realizaram um teste incremental (re-teste: n=12), com velocidade inicial de 5,5 km.h-1 e incrementos de 0,5 km.h-1 a cada minuto, até a exaustão. Medidas contínuas de FC e trocas gasosas foram convertidas para médias de 5 e 20 segundos. Somatória dos resíduos quadrados e quadrado médio do erro foram usados para verificar o melhor ajuste. A relação FC/CTC foi melhor representada pelo modelo Lin2 no grupo teste e re-teste (p<0,05). Foi possível identificar um ponto de deflexão de FC, utilizando o modelo Lin2 (limiar de FC) em todos os indivíduos no teste (164 ± 16,6 bpm; 83,6 por cento FC MÁX) e no re-teste (162 ± 20,0 bpm; 83,9 por cento FC MÁX). O limiar de FC (Lin2PDFC) ocorreu a 9,2 ± 1,3 km.h-1 (67,9 por cento VelMÁX) e foi menor que o LV2 (LV2= 10,6 ± 1,5 km.h-1; 77,3 por cento VelMÁX; p< 0,05), mas não diferente de LV1 (8,4 ± 1,2 km.h-1; 61,6 por cento VelMÁX; p> 0,05). Durante teste incremental em esteira, a relação FC/CTC parece ser bem descrita por uma função linear com 2 segmentos de reta, a qual permite a determinação de um limiar de FC que se aproxima do LV1.

The objective of this study was to analyze the heart rate (HR) profile plotted against incremental workloads (IWL) during a treadmill test using three mathematical models [linear, linear with 2 segments (Lin2), and sigmoidal], and to determine the best model for the identification of the HR threshold that could be used as a predictor of ventilatory thresholds (VT1 and VT2). Twenty-two men underwent a treadmill incremental test (retest group: n=12) at an initial speed of 5.5 km.h-1, with increments of 0.5 km.h-1 at 1-min intervals until exhaustion. HR and gas exchange were continuously measured and subsequently converted to 5-s and 20-s averages, respectively. The best model was chosen based on residual sum of squares and mean square error. The HR/IWL ratio was better fitted with the Lin2 model in the test and retest groups (p<0.05). The Lin2 model permitted the identification of the HR threshold (Lin2HRDP) in all subjects of the test (164 ± 16.6 bpm; 83.6 percent HR MAX) and retest groups (162 ± 20.0 bpm; 83.9 percent HR MAX). Lin2HRDP (9.2 ± 1.3 km.h-1; 67.9 percent speedMAX) was lower than VT2 (10.6 ± 1.5 km.h-1, 77.3 percent speedMAX; p<0.05), but did not differ from VT1 (8.4 ± 1.2 km.h-1, 61.6 percent speedMAX; p>0.05). During a treadmill incremental test, the HR/IWL ratio seems to be better fitted with a Lin2 model, which permits to determine the HR threshold that coincides with VT1.