Article

# Gross Efficiency and Cycling Economy Are Higher in the Field as Compared with on an Axiom Stationary Ergometer

Authors:
• University of Champagne-Ardenne (France)
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## Abstract

This study was designed to examine the biomechanical and physiological responses between cycling on the Axiom stationary ergometer (Axiom, Elite, Fontaniva, Italy) vs. field conditions for both uphill and level ground cycling. Nine cyclists performed cycling bouts in the laboratory on an Axiom stationary ergometer and on their personal road bikes in actual road cycling conditions in the field with three pedaling cadences during uphill and level cycling. Gross efficiency and cycling economy were lower (-10%) for the Axiom stationary ergometer compared with the field. The preferred pedaling cadence was higher for the Axiom stationary ergometer conditions compared with the field conditions only for uphill cycling. Our data suggests that simulated cycling using the Axiom stationary ergometer differs from actual cycling in the field. These results should be taken into account notably for improving the precision of the model of cycling performance, and when it is necessary to compare two cycling test conditions (field/laboratory, using different ergometers).

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... Additionally, differences between level ground and uphill terrain have been investigated. [6][7][8] These previous studies focused on gross efficiency, cycling economy, cadence, and seated versus standing position. [6][7][8] Regarding cycling intensity, field-based studies traditionally describe it focusing on heart rate (HR), power output, and blood lactate concentration ([La − ]). ...
... [6][7][8] These previous studies focused on gross efficiency, cycling economy, cadence, and seated versus standing position. [6][7][8] Regarding cycling intensity, field-based studies traditionally describe it focusing on heart rate (HR), power output, and blood lactate concentration ([La − ]). 2,4,5 However, HR is influenced by cardiovascular drift, glycogen depletion, and environmental factors 1 and as such can result in underestimation of cycling intensity. ...
... While power-output pattern has been investigated extensively in road cycling, in both laboratory and field-based conditions, cardiovascular and respiratory responses have not been widely assessed in outdoor cycling conditions. Oxygen uptake (VO 2 ) has been measured in field-based trials using a portable system, 6,7,8 although investigated distances are shorter than in traditional roadcycling competitions. A recent study was the first to report the physiological demands of a real off-road cycling competition through the assessment of respiratory parameters. ...
Article
Purpose: While a number of studies have researched road cycling performance, few studies have attempted to investigate the physiological response in field conditions. Therefore, the purpose of this study was to describe the physiological and performance profile of an uphill time-trial frequently used in cycling competitions. Methods: Fourteen elite road cyclists (mean±SD: age 25±6 years, height 174±4.2 cm, body mass 64.4±6.1 kg and fat mass 7.48±2.82%) performed a graded exercise test until exhaustion to determine maximal parameters. They then completed a field-based uphill time-trial in a 9.2 km first category mountain pass with a 7.1% slope. Oxygen uptake (VO2), power output, heart rate, lactate concentration and perceived exertion variables were measured throughout the field-based test. Results: During the uphill time-trial, mean power output and velocity were: 302±7 W (4.2±0.1 W·kg(-1)) and 18.7±1.6 km/h, respectively. Mean VO2 and heart rate were: 61.6±2.0 ml·kg(-1)·min(-1) and 178±2 bpm, respectively. Values were significantly affected by the first, second, sixth and final kilometers (p<0.05). Lactate concentration and perceived exertion were 10.87±1.12 mmol·l(-1) and 19.1±0.1, respectively, at the end of the test, being significantly difference from baseline measures. Conclusion: The studied uphill time-trial is performed at 90% of maximum heart rate and VO2 and at 70% of maximum power output. To our knowledge, this is the first study assessing cardiorespiratory parameters combined with measures of performance, perceived exertion and biochemical variables during a field-based uphill time-trial in elite cyclists.
... In equation 6, C is the energy cost, CR is the rolling coefficient, m the body mass of the bicycle-cyclist system, g the gravitational acceleration, v the mean velocity over the race, ρ the air density, A is the surface area and Cd the drag coefficient and η the gross efficiency. The assumed gross efficiency of cyclists is 20% (Bertucci, Betik, Duc, & Grappe, 2012) and CR 0.00368 . ...
... To assess the energy cost, a gross efficiency of 20% was defined. That was supported by literature where gross efficiency ranges between 15% and 25% (Bertucci et al., 2012;Ettema & Lorås, 2009). The energy cost varied between 106.89 J/m and 381.40 J/m for booth helmets at the different speeds. ...
... The CIL varies considerably, dependent on mass of the cyclists and gear ratio, 29 with CIL being as much as 50% lower on a Kingcycle ergometer compared with field cycling. The CIL has also been shown to affect the self-selected pedal rate of untrained and trained 30 cyclists. Related to this, studies have suggested that laboratory cycling conditions elicit significantly different crank torque profiles compared with road cycling conditions, partly due to the stiffness and lack of side-to-side movement of the ergometers used in those studies. ...
... The potential impact of on test performance (power output) between the difference conditions, especially at a shorter duration (<60 s) requires further investigation. It remains questionable as to the long-term effects of differing CIL between indoor and outdoor cycling, the subsequent impact that it would have on the physiological and biomechanical performance characteristics as a result, 30 and continues to be a topic for future investigation. ...
Purpose: The purpose of this study was to assess the relationship between typical performance tests among elite and professional cyclists when conducted indoors and outdoors. Methods: Fourteen male cyclists of either UCI Continental or UCI World Tour Q1 level (mean [SD]: age 20.9 [2.8] y, mass 68.13 [7.25] kg) were recruited to participate in 4 test sessions (2 test sessions indoors and 2 test sessions outdoors) within a 14-day period, consisting of maximum mean power testing for durations of 60, 180, 300, and 840 seconds. Results: Across all maximum mean power test durations, the trimmed mean power was higher outdoors compared with indoor testing (P < .05). Critical power was higher outdoors compared with indoors (+19 W, P = .005), while no difference was observed for the work capacity above critical power. Self-selected cadence was 6 revolutions per minute higher indoors versus outdoors for test durations of 60 (P = .038) and 300 seconds (P = .002). Conclusions: These findings suggest that maximal power testing in indoor and outdoor settings cannot be used interchangeably. Furthermore, there was substantial individual variation in the difference between indoor and outdoor maximum mean powers, across all time durations, further highlighting the difficulty of translating results from indoor testing to outdoor, on an individual level in elite populations.
... In our study, we have used a force-velocity test on an axiom stationary ergometer (Bertucci et al., 2012) with a constant brake force. The cyclists ride their bicycles during the test. ...
... The subject uses his bicycle, fitted with the Powertap, which was connected to the stationary axiom ergometer (Elite, Fontaniva, Italy). The axiom is an electromagnetically braked computerized ergometer, by fixing the rear wheel in the stand of the stationary axiom ergometer by the rear wheel quick-release skewer (Bertucci et al., 2012). The data are measured with a Powertap G3 hub power meter with an accuracy of 1,5%, the Powertap is used to measure the mechanical power at the rear hub of the bicycle (Bertucci et al., 2005). ...
... In equation 6, C is the energy cost, CR is the rolling coefficient, m the body mass of the bicycle-cyclist system, g the gravitational acceleration, v the mean velocity over the race, ρ the air density, A is the surface area and Cd the drag coefficient and η the gross efficiency. The assumed gross efficiency of cyclists is 20% (Bertucci, Betik, Duc, & Grappe, 2012) and CR 0.00368 . ...
... To assess the energy cost, a gross efficiency of 20% was defined. That was supported by literature where gross efficiency ranges between 15% and 25% (Bertucci et al., 2012;Ettema & Lorås, 2009). The energy cost varied between 106.89 J/m and 381.40 J/m for booth helmets at the different speeds. ...
Article
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The aim of this study was to assess and compare by numerical simulations and analytical models the resistive forces, mechanical power, energy cost and velocity using two different types of road helmets (standard vs aero road helmet). An elite cyclist was scanned on the racing bicycle, wearing his competition gear and helmets. Numerical simulations by Computational Fluid Dynamics were carried-out at 11.11 m/s (40 km/h) and 20.83 m/s (75 km/h) to extract the drag force. The mechanical power and energy cost were estimated by analytical procedures. The drag forces were between 9.93 N and 66.96 N across the selected speeds and helmets. The power to overcome drag were 182.19 W and 1121.40 W. The total power lower and higher values were 271.05 W and 1558.02 W. The energy cost estimation was between 106.89 J/m and 381.40 J/m across the different speeds and helmets. The standard helmet imposed higher drag and demanded more power.
... Quantification of joint kinematics in three dimensions is essential to understanding and characterizing human body movements. It enables identification of pathological movements by comparison with asymptomatic movements [1] or the determination of performance-relevant parameters in sports [2]. For both purposes, it appears pertinent to assess kinematics in the real environment in order to characterize movement in realistic conditions [3,4]. ...
... However, this system is cumbersome to set up and has a relatively small measurement field. Therefore, these systems cannot be used outside laboratories to follow subjects in ecological environments [3,4] for clinical objectives, or in situ for sports application [2], as previously stated. ...
Article
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Kinematic analysis is indispensable to understanding and characterizing human locomotion. Thanks to the development of inertial sensors based on microelectronics systems, human kinematic analysis in an ecological environment is made possible. An important issue in human kinematic analyses with inertial sensors is the necessity of defining the orientation of the inertial sensor coordinate system relative to its underlying segment coordinate system, which is referred to sensor-to-segment calibration. Over the last decade, we have seen an increase of proposals for this purpose. The aim of this review is to highlight the different proposals made for lower-body segments. Three different databases were screened: PubMed, Science Direct and IEEE Xplore. One reviewer performed the selection of the different studies and data extraction. Fifty-five studies were included. Four different types of calibration method could be identified in the articles: the manual, static, functional, and anatomical methods. The mathematical approach to obtain the segment axis and the calibration evaluation were extracted from the selected articles. Given the number of propositions and the diversity of references used to evaluate the methods, it is difficult today to form a conclusion about the most suitable. To conclude, comparative studies are required to validate calibration methods in different circumstances.
... where, C is the energy cost, CR is the rolling coefficient, m the body mass of the bicycle-cyclist system, g the gravitational acceleration, v the mean velocity over the race, ρ the air density, A is the surface area and C d the coefficient of drag and η the gross efficiency. The assumed gross efficiency of the cyclist was 20% (Bertucci et al., 2012) and CR 0.00368 . The differences between positions were presented with two ranges of speed: 1 to 11 m/s and 12 to 22 m/s. ...
... Energy cost was computed based on the procedure reported by Candau et al. (1999). The gross efficiency was assumed to be 20% (Ettema and Lorås, 2009;Bertucci et al., 2012). Again, energy cost depends on drag, rolling resistance and gross efficiency . ...
Article
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The aim of this study was to use numerical simulations and analytical procedures to compare a cyclist's performance in three different cycling positions. An elite level road cyclist competing at a national level was recruited for this research. The bicycle was 7 kg and the cyclist 55 kg. A 3D scan was taken of the subject on the competition bicycle, wearing race gear and helmet in the upright position, in the handlebar drops (dropped position) and leaning on the elbows (elbows position). Numerical simulations by computer fluid dynamics in Fluent CFD code assessed the coefficient of drag at 11.11 m/s. Following that, a set of assumptions were employed to assess cycling performance from 1 to 22 m/s. Drag values ranged between 0.16 and 99.51 N across the different speeds and positions. The cyclist mechanical power in the elbows position differed from the upright position between 0 and 23% and from the dropped position from 0 to 21%. The cyclist's energy cost in the upright position differed 2 to 16% in comparison to the elbows position and the elbows position had less 2 to 14% energy cost in comparison to the dropped position. The estimated time of arrival was computed for a 220,000 m distance and it varied between 7,715.03 s (2 h:8 min:24 s) and 220,000 s (61 h:6 min:40 s) across the different speeds and positions. In the elbows position, is expected that a cyclist may improve the winning time up to 23% in comparison to he upright and dropped position across the studied speeds.
... Mean velocity in tours is near 11.1 m/s (~40 km/h) [28,29]. Knowing that, velocities up to 13 m/s with increments of 1 m/s. ...
... In Equation (2), Ec is the energy cost, CR is the rolling coefficient, m the body mass of the bicyclecyclist system, g the gravitational acceleration, v the mean velocity over the race, ρ the air density, A is the surface area and CD the drag coefficient and η the gross efficiency. The assumed gross efficiency of cyclists is 20% [29] and CR 0.00368 [14]. ...
Article
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Background and Objectives: The aim of this study was to assess and compare the drag and energy cost of three cyclists assessed by computational fluid dynamics (CFD) and analytical procedures. Materials and methods: A transradial (Tr) and transtibial (Tt) were compared to a full-body cyclist at different speeds. An elite male cyclist with 65 kg of mass and 1.72 m of height volunteered for this research with his competition cloths, helmet and bicycle with 5 kg of mass. A 3D model of the bicycle and cyclist in the upright position was obtained for numerical simulations. Upon that, two more models were created, simulating elbow and knee-disarticulated athletes. Numerical simulations by computational fluid dynamics and analytical procedures were computed to assess drag and energy cost, respectively. Results: One-Way ANOVA presented no significant differences between cyclists for drag (F = 0.041; p = 0.960; η 2 = 0.002) and energy cost (F = 0.42; p = 0.908; η 2 = 0.002). Linear regression presented a very high adjustment for absolute drag values between able-bodied and Tr (R 2 = 1.000; Ra 2 = 1.000; SEE = 0.200) and Tt (R 2 = 1.00; Ra 2 = 1.000; SEE = 0.160). The linear regression for energy cost presented a very high adjustment for absolute values between able-bodied and Tr (R 2 = 1.000; Ra 2 = 1.000; SEE = 0.570) and Tt (R 2 = 1.00; Ra 2 = 1.00; SEE = 0.778). Conclusions: This study suggests that drag and energy cost was lower in the able-bodied, followed by the Tr and Tt cyclists.
... These coaches observed that the measured performance under laboratory conditions was generally lower by 30 to 50 W over a 20-minute TT than in training tests under outdoor conditions (especially under UP). 30 In this study, PO in the UP TT4 test was 41 W (11.2%) higher than in LG. This result is higher than the results of Padilla et al, 6 Vogt et al, 7 and Nimmerichter et al, 9 who noted smaller differences of 4.5%, 3.6%, and 5.4%, respectively. ...
... Because pedaling cadence was free, the results indicate significant differences in cadence under all cycling conditions. Our findings agree with those of Emanuele and Denoth, 32 who studied the influence of road incline and body position on the powercadence relationship, as well as with those of Bertucci et al, 30 who showed that the preferred pedaling cadence was higher under CE than under LG (+11.1%) and UP (+30%). Moreover, it has been shown by Swain and Wilcox 33 that UP cycling is more economical at high versus low cadence even if the lower cadence studied (41 rpm) was never measured in competition conditions. ...
Article
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Purpose: This study aimed to 1) analyse the effect of the pedalling activity in different 4-min time trials (TT4) (laboratory and field conditions) and 2) make the comparison between TT4 and MAP determined from the classical incremental exercise test in laboratory. It was hypothesized that the exercises performed on the field would determine higher physical (PO) and mental (AL) involvements due to different environmental conditions. Methods: Sixteen male cyclists underwent an incremental test to exhaustion and three TT4 under different conditions: cycle ergometer (CE), level ground (LG), and uphill (UP). Results: Correlation was observed for PO with a trivial effect size and narrow limits of agreement (LoA) between MAP and CE TT4 (r = 0.96, p < 0.001). The comparison between the CE, LG, and UP tests indicates that PO was significantly higher in UP compared with CE (+8.0% p < 0.001) and LG (+11.0% p < 0.001). Conclusions: The results suggest that PO depends on the nature of the pedalling activity. Moreover, PO under CE TT4 is a relevant predictor of MAP. It seems to be important to measure the MAP by taking into account the cycling conditions, considering that coaches and scientists use this parameter to assess the aerobic potential of athletes and determine the exercise intensities useful for monitoring adaptation to training.
... However, in a recent study GE was significantly lower at a gradient of 8% compared to both, 4% and flat cycling on a motorized treadmill. 14 Despite the high ecological validity of field cycling 13,15 only a few studies examined GE in field conditions 16,17 . Millet et al,17 found no differences in GE between uphill and flat cycling, in seated and standing positions and Bertucci et al, 16 reported GE to be 10% lower in laboratory compared to field conditions when cycling in seated position. ...
... 14 Despite the high ecological validity of field cycling 13,15 only a few studies examined GE in field conditions 16,17 . Millet et al,17 found no differences in GE between uphill and flat cycling, in seated and standing positions and Bertucci et al, 16 reported GE to be 10% lower in laboratory compared to field conditions when cycling in seated position. However, both studies used freely chosen cadences, which might impact on their results as variations in cadence affect GE. ...
Article
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Whilst a number of studies investigated gross efficiency (GE) in laboratory conditions, few studies have analyzed GE in field conditions. Therefore, the aim of this study was to analyze the effect of gradient and cadence on GE in field conditions. Thirteen trained cyclists (mean ± SD age: 23.3 ± 4.1 years; stature: 177.0 ± 5.5 cm; body mass: 69.0 ± 7.2 kg; VO2max 68.4 ± 5.1 mL·min-1·kg-1) completed an incremental graded exercise test to determine the ventilatory threshold (VT) and 4 field trials of 6 min duration at 90% of VT on flat (1.1%) and uphill terrain (5.1%) with two different cadences (60 and 90 rev·min-1). Oxygen uptake was measured with a portable gas analyzer and power output was controlled with a mobile power crank, which was mounted on a 26-inch mountain bike. GE was significantly affected by cadence (20.6 ± 1.7% vs. 18.1 ± 1.3% at 60 and 90 rev·min-1, respectively; P<0.001) and terrain (20.0 ± 1.5% vs. 18.7 ± 1.7% at flat and uphill cycling, respectively; P=0.029). The end-exercise oxygen uptake was 2536 ± 352 mL·min-1 and 2594 ± 329 mL·min-1 for flat and uphill cycling, respectively (P=0.489). There was a significant difference in end-exercise oxygen uptake between the 60 (2352 ± 193 mL·min-1) and the 90 rev·min-1 (2778 ± 431 mL·min-1) (P<0.001). This findings support previous laboratory based studies demonstrating reductions in GE with increasing cadence and gradient that might be attributed to changes in muscle activity pattern.
... The muscular efficiency of any effort can be defined as the ratio between the energy input relative to the mechanical work completed [24]. At exercise intensities below the critical speed/power (CS/CP, for reviews see [25][26][27]), defined as the highest oxidative metabolic rate that can be sustained during continuous exercise [25], we can accurately measure both the mechanical speed/power and metabolic cost, and therefore economy of locomotion [28] and gross mechanical efficiency [29]. However, beyond this key intensity landmark, the anaerobic energy contribution cannot be accurately quantified [18]. ...
Article
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Many individual and team sport events require extended periods of exercise above the speed or power associated with maximal oxygen uptake (i.e., maximal aerobic speed/power, MAS/MAP). In the absence of valid and reliable measures of anaerobic metabolism, the anaerobic speed/power reserve (ASR/APR) concept, defined as the difference between an athlete’s MAS/MAP and their maximal sprinting speed (MSS)/peak power (MPP), advances our understanding of athlete tolerance to high speed/power efforts in this range. When exercising at speeds above MAS/MAP, what likely matters most, irrespective of athlete profile or locomotor mode, is the proportion of the ASR/APR used, rather than the more commonly used reference to percent MAS/MAP. The locomotor construct of ASR/APR offers numerous underexplored opportunities. In particular, how differences in underlying athlete profiles (e.g., fiber typology) impact the training response for different ‘speed’, ‘endurance’ or ‘hybrid’ profiles is now emerging. Such an individualized approach to athlete training may be necessary to avoid ‘maladaptive’ or ‘non-responses’. As a starting point for coaches and practitioners, we recommend upfront locomotor profiling to guide training content at both the macro (understanding athlete profile variability and training model selection, e.g., annual periodization) and micro levels (weekly daily planning of individual workouts, e.g., short vs long intervals vs repeated sprint training and recovery time between workouts). More specifically, we argue that high-intensity interval training formats should be tailored to the locomotor profile accordingly. New focus and appreciation for the ASR/APR is required to individualize training appropriately so as to maximize athlete preparation for elite competition.
... The relative contribution of the baseline energy expenditure in the GE calculations decreases gradually with increasing work intensity; hence, it should be expected that the GE during cycling is related to work intensity. This is in line with a previous study that reported a positive relationship between cyclists' GE and crank inertial load (Bertucci et al. 2012). In the running test, there was no difference between GE run and DE run , which means that triathletes' GE during moderate uphill running does not change significantly with increasing work rate. ...
Article
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PurposeTo investigate the gross efficiency (GE) and delta efficiency (DE) during cycling and running in elite triathletes.Methods Five male and five female elite triathletes completed two incremental treadmill tests with an inclination of 2.5° to determine their GE and DE during cycling and running. The speed increments between the 5-min stages were 2.4 and 0.6 km h−1 during the cycling and running tests, respectively. For each test, GE was calculated as the ratio between the mechanical work rate (MWR) and the metabolic rate (MR) at an intensity corresponding to a net increase in blood-lactate concentration of 1 mmol l−1. DE was calculated by dividing the delta increase in MWR by the delta increase in MR for each test. Pearson correlations and paired-sample t tests were used to investigate the relationships and differences, respectively.ResultsThere was a correlation between GEcycle and GErun (r = 0.66; P = 0.038; R2 = 0.44), but the correlation between DEcycle and DErun was not statistically significant (r = − 0.045; P = 0.90; R2 = 0.0020). There were differences between GEcycle and GErun (t = 80.8; P < 0.001) as well as between DEcycle and DErun (t = 27.8; P < 0.001).Conclusions Elite triathletes with high GE during running also have high GE during cycling, when exercising at a treadmill inclination of 2.5°. For a moderate uphill incline, elite triathletes are more energy efficient during cycling than during running, independent of work rate.
... (Fregly et al., 1996 ;Hansen et al., 2002 ;Bertucci et al., 2007). Au niveau de la cadence de pédalage, les résultats obtenus sont en accord avec les précédentes études puisque la cadence sur cyclo-ergomètre était supérieure à celle sur terrain plat (+11%), toutes les deux supérieures à celle sur terrain montant (+30%) (Bertucci et al., 2012 ;Emanuele et Denoth, 2012). ...
Thesis
Ce travail de thèse s’est déroulé dans le cadre d’une convention CIFRE entre mon laboratoire de rattachement C3S (EA4660) et le département Recherche et Développement (R&D) de l’équipe cycliste professionnelle FDJ. Les différentes études que nous avons conduites se sont articulées autour de l’amélioration de la performance sportive chez le cycliste à travers une variable centrale qui est la puissance mécanique qu’il développe lors de la locomotion (Pméca) selon deux axes principaux : 1) l’évaluation et le suivi du potentiel physique avec pour but l’amélioration du processus d’entraînement et 2) l’optimisation de l’interface homme – machine à partir de l’analyse du matériel et des équipements utilisés par les cyclistes dans l’équipe FDJ.
... Estas diferencias podrían afectar tanto a la geometría muscular, la comodidad, el rendimiento del pedaleo, e incluso a la incidencia de lesiones (Disley & Li, 2014). Muy a menudo, los cicloergómetros tienen diferentes características en los volantes de inercia que implican valores de carga inercial del eje del pedalier significativamente más bajos que los existentes en condiciones reales en el ciclismo en carretera (Bertucci, Betik, Duc, & Grappe, 2012;Fregly, Zajac, & Dairaghi, 2000). Para optimizar la calidad de las pruebas de evaluación de aptitud, es importante usar un cicloergómetro que permita el control de las características de inercia con el fin de simular las condiciones reales de ciclismo, de modo que disponga de una adecuada validez ecológica, fiabilidad, así como que permita la medición de potencia con la suficiente sensibilidad. ...
Thesis
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... Given that ergometer versus outdoor cycling can affect cycling physiological measures (Bertucci et al., 2012) and pedalling biomechanics (Bertucci et al., 2007), cyclists brought their road bikes to the laboratory the first week of testing. Bike setup parameters were recorded and employed to individualize setup on a Lode cycling ergometer (Excalibur Sport, Lode B.V., Groningen, Netherlands), with all cyclists using their habitual cleats. ...
Article
Kinesiology-type tape (KTT) has become popular in sports for injury prevention, rehabilitation, and performance enhancement. Many cyclists use patella KTT; however, its benefits remain unclear, especially in uninjured elite cyclists. We used an integrated approach to investigate acute physiological, kinematic, and electromyographic responses to patella KTT in twelve national-level male cyclists. Cyclists completed four, 4-minute submaximal efforts on an ergometer at 100 and 200 W with and without patella KTT. Economy, energy cost, oxygen cost, heart rate, efficiency, 3D kinematics, and lower-body electromyography signals were collected over the last minute of each effort. Comfort levels and perceived change in knee stability and performance with KTT were recorded. The effects of KTT were either unclear, non-significant, or clearly trivial on all collected physiological and kinematic measures. KTT significantly, clearly, and meaningfully enhanced vastus medialis peak, mean, and integrated electromyographic signals, and vastus medialis-to-lateralis activation. Electromyographic measures from biceps femoris and biceps-to-rectus femoris activation ratio decreased in either a significant or clinically meaningful manner. Despite most cyclists perceiving KTT as comfortable, increasing stability, and improving performance, the intervention exerted no considerable effects on all physiological and kinematic measures. KTT did alter neuromuscular recruitment, which has potential implications for injury prevention.
... Balance and coordination, therefore, are paramount to ensure that the cyclist maintain momentum and stay upright when cycling on Rollers. To date, there have been very few scientific studies examining the pros, cons, and benefits received when using indoor cycling equipment (4,2,10). ...
Article
The primary aim of this investigation was to determine which cycling training device, Rollers or Trainers, was most effective in improving 10-km time trial. Eight male and 6 female volunteers (N = 14; age = 23.6 ± 4.6 yrs; height = 172.7 ± 9.9 cm; body mass = 68.4 ± 10.4 kg; % body fat = 16.9 ± 7.7; VO2max = 61.0 ± 9.4 ml·kg⁻¹·min⁻¹) provided informed consent prior to participation. Participants performed a10-km time trial at baseline and were then randomly assigned into one of three groups: Rollers (R), Trainers (T), or Control (C). Participants assigned to the R or T groups attended 24 supervised workout sessions throughout an 8-wk period (F: 3 days/week; I: 65–80% HRmax; D: 40 min; M: R or T). There were no significant differences in baseline 10-km time trial between R, T, and C groups [F(2,12) = 0.34, p = .72]. There was a significant difference in 10-km time trial improvement between groups post-assessment when controlling for baseline values (F = 17.04, p <.001). R participants improved by 20.4s [t(4) = 4.86, p = .008] and T participants improved by 12.8s [t(4) = 4.57, p = .01], while there was no significant improvement for subjects in C. Participants using R and T displayed significant decrements in time with respect to the 10-km time trial. However, R had a greater improvement in 10-km time trial when compared to T.
... 6 To improve performance by creating high pedal forces with limited trunk movements, and to avoid the standing position in order to decrease aerodynamic drag 30 It is important to note that pedaling on a cycling ergometer and cycling in the field are two different things. 32 Even if the former is a common practice for rehabilitation and training, it does not allow lateral bicycle oscillations, which are supposed to interfere with the pedaling technique. Thus, upper limb kinetics may presumably be modified in the field, particularly in the standing position in which roll angles up to 24° have been observed and may increase the upper limb moments specifically in the frontal plane. ...
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Several suggestions of the upper limb involvement in cycling exist, but to date, no study has quantified upper limb kinetics in this task. The aim of this study was to determine how crank power and pedaling position (seated or standing) affect upper limb kinetics. Handlebar loadings and upper limb kinematics were collected from 17 participants performing seated or standing pedaling trials in a random order at 6 crank powers ranging from 20% (112±19W) to 120% (675±113W) of their spontaneous sit-to-stand transition power. An inverse dynamics approach was used to compute 3D moments, powers, and works at the wrist, elbow, and shoulder joints. Over29 parameters investigated, increases in crank power were associated with increases in the magnitudes of 23 and 20 of the kinetic variables assessed in seated and standing positions, respectively. The standing position was associated with higher magnitudes of upper limb kinetics. These results suggest that both upper and lower limbs should be considered in future models in order to better understand whole body coordination in cycling.
... It is important to note some limitations of the present study. The use of a cycling ergometer is a common practice for testing, rehabilitation and training, and as stated above differs with cycling in the field (Bertucci et al., 2012). Reproducing this protocol during field cycling may therefore be aimed in future investigations. ...
... This could require increased muscle activation, including increased common drive from supraspinal centres (De Luca & Erim 1994) to the central pattern generator (Minassian et al. 2007), and contribute to larger net excitability of the central pattern generator. A later study (Bertucci et al. 2012) supported the findings by Hansen et al. (2002b) of an increasing effect of crank inertial load on freely chosen pedalling frequency by showing a tendency (P = 0.06) for a positive correlation between crank inertial load during road cycling at consistent power output (on average approx. 245 W) and freely chosen pedalling frequency in nine male cyclists. ...
Thesis
... Using the linear work limit (W lim ) vs. time limit (T lim ) relation for the estimation of CP1 values and the inverse time ( Data suggest that laboratory and fi eld tests might produce diff erent fi ndings. For example, Jobson et al. [ 27 ] reported higher power output values in the fi eld than in the laboratory at given VO 2 values, while Bertucci et al. [ 3 ] found an increased gross efficiency and cycling economy in the fi eld when compared to the laboratory. While conditions in the laboratory are more controllable, providing greater reliability, fi eld tests have the advantage of providing greater ecological validity [ 21 , 30 ] . ...
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The purpose of this study was to investigate the level of agreement between laboratory-based estimates of critical power (CP) and results taken from a novel field test. Subjects were fourteen trained cyclists (age 40±7 yrs; body mass 70.2±6.5 kg; V̇O2max 3.8±0.5 L · min-1). Laboratory-based CP was estimated from 3 constant work-rate tests at 80%, 100% and 105% of maximal aerobic power (MAP). Field-based CP was estimated from 3 all-out tests performed on an outdoor velodrome over fixed durations of 3, 7 and 12 min. Using the linear work limit (Wlim) vs. time limit (Tlim) relation for the estimation of CP1 values and the inverse time (1/t) vs. power (P) models for the estimation of CP2 values, field-based CP1 and CP2 values did not significantly differ from laboratory-based values (234±24.4 W vs. 234±25.5 W (CP1); P<0.001; limits of agreement [LOA], -10.98-10.8 W and 236±29.1 W vs. 235±24.1 W (CP2); P<0.001; [LOA], -13.88-17.3 W. Mean prediction errors for laboratory and field estimates were 2.2% (CP) and 27% (W'). Data suggest that employing all-out field tests lasting 3, 7 and 12 min has potential utility in the estimation of CP.
... Using the linear work limit (W lim ) vs. time limit (T lim ) relation for the estimation of CP1 values and the inverse time ( Data suggest that laboratory and fi eld tests might produce diff erent fi ndings. For example, Jobson et al. [ 27 ] reported higher power output values in the fi eld than in the laboratory at given VO 2 values, while Bertucci et al. [ 3 ] found an increased gross efficiency and cycling economy in the fi eld when compared to the laboratory. While conditions in the laboratory are more controllable, providing greater reliability, fi eld tests have the advantage of providing greater ecological validity [ 21 , 30 ] . ...
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The purpose of this study was to investigate the level of agreement between field-based and laboratory-based estimates of critical power (CP) in cycling. Subjects were fourteen trained cyclists (age 40 ± 7 yrs; body mass 70.2 ± 6.5 kg; max 3.8 ± 0.48 L·min-1). Laboratory-based CP was estimated from three constant work-rate tests performed on a cycle ergometer at 80%, 100% and 105% of maximal aerobic power (MAP). Field-based CP was estimated from three all-out tests performed on an outdoor velodrome over fixed durations of 3, 7 and 12 minutes. Using the linear work limit (Wlim) versus time limit (Tlim) relation and the inverse time (1/t) versus power (P) models, field-based CP1 and CP2 values did not significantly differ from laboratory-based values (234 ± 24.4W vs. 234 ± 25.5W (CP1); P < 0.001, r2 = 0.95; limits of agreement [LOA], −10.98 to 10.8 W and 236 ± 29.1W vs. 235 ± 24.1W (CP2); P < 0.001, r2 = 0.95 [LOA], -13.88 to 17.3 W. Data suggest that employing all-out field tests lasting 3, 7 and 12minutes has potential utility in the estimation of CP.
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This review presents information that is useful to athletes, coaches and exercise scientists in the adoption of exercise protocols, prescription of training regimens and creation of research designs. Part 2 focuses on the factors that affect cycling performance. Among those factors, aerodynamic resistance is the major resistance force the racing cyclist must overcome. This challenge can be dealt with through equipment technological modifications and body position configuration adjustments. To successfully achieve efficient transfer of power from the body to the drive train of the bicycle the major concern is bicycle configuration and cycling body position. Peak power output appears to be highly correlated with cycling success. Likewise, gear ratio and pedalling cadence directly influence cycling economy/efficiency. Knowledge of muscle recruitment throughout the crank cycle has important implications for training and body position adjustments while climbing. A review of pacing models suggests that while there appears to be some evidence in favour of one technique over another, there remains the need for further field research to validate the findings. Nevertheless, performance modelling has important implications for the establishment of performance standards and consequent recommendations for training.
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Many scientists and coaches are interested in mechanical power produced during cycling, and use Schoberer Rad Me\technik (SRM) bicycle power cranks to obtain this data. However, it has been expensive and difficult to calibrate SRM cranks, causing much of the collected data to be unreliable. We present a static method, derived from first principles, for obtaining a calibration factor for SRM cranks. A known mass and lever arm (chainring of a known diameter) are used to apply a known torque load to the instrument in four positions, and the output frequencies are used to calculate the calibration factor in Hz/Nm. The reproducibility of this method is ±0.01 Hz/Nm, which is acceptable for the application of the instrument, which is measurement of mechanical power application by cyclists at the crank. The method is reliable, inexpensive, and easy to set up, and will allow higher confidence in data collected using SRM power cranks. We recommend calibration of the power meter once every six months because of the measured drift of the calibration factor over time.
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The aims of this study were to compare the physiological demands of laboratory- and road-based time-trial cycling and to examine the importance of body position during laboratory cycling. Nine male competitive but non-elite cyclists completed two 40.23-km time-trials on an air-braked ergometer (Kingcycle) in the laboratory and one 40.23-km time-trial (RD) on a local road course. One laboratory time-trial was conducted in an aerodynamic position (AP), while the second was conducted in an upright position (UP). Mean performance speed was significantly higher during laboratory trials (UP and AP) compared with the RD trial (P < 0.001). Although there was no difference in power output between the RD and UP trials (P > 0.05), power output was significantly lower during the AP trial than during both the RD (P = 0.013) and UP trials (P = 0.003). Similar correlations were found between AP power output and RD power output (r = 0.85, P = 0.003) and between UP power output and RD power output (r = 0.87, P = 0.003). Despite a significantly lower power output in the laboratory AP condition, these results suggest that body position does not affect the ecological validity of laboratory-based time-trial cycling.
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This study examined the hypothesis that running speed over 800- and 1,500-m races is regulated by the prevailing anaerobic (oxygen independent) store (ANS) at each instant of the race up until the all-out phase of the race over the last several meters. Therefore, we hypothesized that the anaerobic power that allows running above the speed at maximal oxygen uptake (VO2max) is regulated by ANS, and as a consequence the time limit at the anaerobic power (tlim PAN=ANS/PAN) is constant until the final sprint. Eight 800-m and seven 1,500-m male runners performed an incremental test to measure VO2max and the minimal velocity associated with the attainment of VO2max (vVO2max), referred to as maximal aerobic power, and ran the 800-m or 1,500-m race with the intent of achieving the lowest time possible. Anaerobic power (PAN) was measured as the difference between total power and aerobic power, and instantaneous ANS as the difference between end-race and instantaneous accumulated oxygen deficits. In 800 m and 1,500 m, tlim PAN was constant during the first 70% of race time in both races. Furthermore, the 1,500-m performance was significantly correlated with tlim PAN during this period (r=-0.92, P<0.01), but the 800-m performance was not (r=-0.05, P=0.89), although it was correlated with the end-race oxygen deficit (r=-0.70, P=0.05). In conclusion, this study shows that in middle-distance races over both 800 m and 1,500 m, the speed variations during the first 70% of the race time serve to maintain constant the time to exhaustion at the instantaneous anaerobic power. This observation is consistent with the hypothesis that at any instant running speed is controlled by the ANS remaining.
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Errors in statistical analysis of multiple dependent variables and in documenting the size of effects are common in the scientific and biomechanical literature. In this paper, I review these errors and several solutions that can improve the validity of sports biomechanics research reports. Studies examining multiple dependent variables should either control for the inflation of Type I errors (e.g. Holm's procedure) during multiple comparisons or use multivariate analysis of variance to focus on the structure and interaction of the dependent variables. When statistically significant differences are observed, research reports should provide confidence limits or effect sizes to document the size of the effects. Authors of sports biomechanics research reports are encouraged to analyse and present their data accounting for the experiment-wise Type I error rate, as well as reporting data documenting the size or practical significance of effects reaching their standard of statistical significance.
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We focus on the effect of cadence and work rate on energy expenditure and efficiency in cycling, and present arguments to support the contention that gross efficiency can be considered to be the most relevant expression of efficiency. A linear relationship between work rate and energy expenditure appears to be a rather consistent outcome among the various studies considered in this review, irrespective of subject performance level. This relationship is an example of the Fenn effect, described more than 80 years ago for muscle contraction. About 91% of all variance in energy expenditure can be explained by work rate, with only about 10% being explained by cadence. Gross efficiency is strongly dependent on work rate, mainly because of the diminishing effect of the (zero work-rate) base-line energy expenditure with increasing work rate. The finding that elite athletes have a higher gross efficiency than lower-level performers may largely be explained by this phenomenon. However, no firm conclusions can be drawn about the energetically optimal cadence for cycling because of the multiple factors associated with cadence that affect energy expenditure.
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The purpose of this study was to evaluate the effects of continuous and interval training on changes in lactate and ventilatory thresholds during incremental exercise. Seventeen males were assigned to one of three training groups: group 1:55 min continuous exercise at approximately 50% maximum O2 consumption (VO2max); group 2: 35 min continuous exercise at approximately 70% VO2max; and group 3: 10 X 2-min intervals at approximately 105% VO2max interspersed with rest intervals of 2 min. All of the subjects were tested and trained on a cycle ergometer 3 day/wk for 8 wk. Lactate threshold (LT) and ventilatory threshold (VT) (in addition to maximal exercise measures) were determined using a standard incremental exercise test before and after 4 and 8 wk of training. VO2max increased significantly in all groups with no statistically significant differences between the groups. Increases (+/- SE) in LT (ml O2 X min-1) for group 1 (569 +/- 158), group 2 (584 +/- 125), and group 3 (533 +/- 88) were significant (P less than 0.05) and of the same magnitude. VT also increased significantly (P less than 0.05) in each group. However, the increase in VT (ml O2 X min-1) for group 3 (699 +/- 85) was significantly greater (P less than 0.05) than the increases in VT for group 1 (224 +/- 52) and group 2 (404 +/- 85). For group 1, the posttraining increase in LT was significantly greater than the increase in VT (P less than 0.05). We conclude that both continuous and interval training were equally effective in augmenting LT, but interval training was more effective in elevating VT.(ABSTRACT TRUNCATED AT 250 WORDS)
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Bicycle pedaling has been studied from both a motor control and an equipment setup and design perspective. In both cases, although the dynamics of the bicycle drive system may have an influence on the results, a thorough understanding of the dynamics has not been developed. This study pursued three objectives related to developing such an understanding. The first was to identify the limitations of the inertial/frictional drive system model commonly used in the literature. The second was to investigate the advantages of an inertial/frictional/compliant model. The final objective was to use these models to develop a methodology for configuring a laboratory ergometer to emulate the drive system dynamics of road riding. Experimental data collected from the resulting road-riding emulator and from a standard ergometer confirmed that the inertial/frictional model is adequate for most studies of road-riding mechanics or pedaling coordination. However, the compliant model was needed to reproduce the phase shift in crank angle variations observed experimentally when emulating the high inertia of road riding. This finding may be significant for equipment setup and design studies where crank kinematic variations are important or for motor control studies where fine control issues are of interest.
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The purpose of this study was to assess reliability of both indoor and outdoor 40 km time-trial cycling performance. Eight trained cyclists completed three indoor 40 km time-trials on an air-braked ergometer (Kingcycle) and three outdoor 40 km time-trials on a local course. Power output was measured for all trials using the SRM powermeter. Mean performance time across three indoor trials was 54.21 +/- 2.59 (min:sec) and was significantly different (P<0.05) to mean time across three outdoor trials (57.29 +/- 3.22 min:sec). However, there was no significant difference (P = 0.34) for mean power across three indoor trials (303+/-35W) when compared to outdoor performances (312 +/- 23 W). Within-subject variation for mean power output expressed as a coefficient of variation (CV) improved in both indoors and outdoors for trials 2 and 3 (CV = 1.9%, 95% CI 1.0 - 3.4 and CV = 2.1 %, 95 % CI 1.1 - 3.8) when compared to trials 1 and 2 (CV=2.1%, 95% CI 1.2-3.8 and CV=2.4%, 95% CI 1.3-4.3). These findings indicate that power output measured using the SRM powermeter is highly reproducible for both laboratory-based and actual 40 km time-trial cycling performance.
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This study was designed to examine the effects of cycling position (seated or standing) during level-ground and uphill cycling on gross external efficiency (GE) and economy (EC). Eight well-trained cyclists performed in a randomized order five trials of 6-min duration at 75% of peak power output either on a velodrome or during the ascent of a hill in seated or standing position. GE and EC were calculated by using the mechanical power output that was measured by crankset (SRM) and energy consumption by a portable gas analyzer (Cosmed K4b(2)). In addition, each subject performed three 30-s maximal sprints on a laboratory-based cycle ergometer or in the field either in seated or standing position. GE and EC were, respectively, 22.4 +/- 1.5% (CV = 5.6%) and 4.69 +/- 0.33 kJ x L(-1) (CV = 5.7%) and were not different between level seated, uphill seated, or uphill standing conditions. Heart rate was significantly ( < 0.05) higher in standing position. In the uphill cycling trials, minute ventilation was higher ( < 0.05) in standing than in seated position. The average 30-s power output was higher ( < 0.01) in standing (803 +/- 103 W) than in seated position (635 +/- 123 W) or on the stationary ergometer (603 +/- 81 W). Gradient or body position appears to have a negligible effect on external efficiency in field-based high-intensity cycling exercise. Greater short-term power can be produced in standing position, presumably due to a greater force developed per revolution. However, the technical features of the standing position may be one of the most determining factors affecting the metabolic responses.
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To determine the effects of changes in pedaling frequency on the gross efficiency (GE) and other physiological variables (oxygen uptake (VO2), HR, lactate, pH, ventilation, motor unit recruitment estimated by EMG) of professional cyclists while generating high power outputs (PO). Following a counterbalanced, cross-over design, eight professional cyclists (age (mean +/- SD): 26 +/- 2 yr, VO2max: 74.0 +/- 5.7 mL x kg x min) performed three 6-min bouts at a fixed PO (mean of 366 +/- 37 W) and at a cadence of 60, 80, and 100 rpm. Values of GE averaged 22.4 +/- 1.7, 23.6 +/- 1.8 and 24.2 +/- 2.0% at 60, 80, and 100 rpm, respectively. Mean GE at 100 rpm was significantly higher than at 60 rpm (P < 0.05). Similarly, mean values of VO2, HR, rates of perceived exertion (RPE), lactate and normalized root-mean square EMG (rms-EMG) in both vastus lateralis and gluteus maximum muscles decreased at increasing cadences. In professional road cyclists riding at high PO, GE/economy improves at increasing pedaling cadences.
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This review presents information that is useful to athletes, coaches and exercise scientists in the adoption of exercise protocols, prescription of training regimens and creation of research designs. Part 2 focuses on the factors that affect cycling performance. Among those factors, aerodynamic resistance is the major resistance force the racing cyclist must overcome. This challenge can be dealt with through equipment technological modifications and body position configuration adjustments. To successfully achieve efficient transfer of power from the body to the drive train of the bicycle the major concern is bicycle configuration and cycling body position. Peak power output appears to be highly correlated with cycling success. Likewise, gear ratio and pedalling cadence directly influence cycling economy/efficiency. Knowledge of muscle recruitment throughout the crank cycle has important implications for training and body position adjustments while climbing. A review of pacing models suggests that while there appears to be some evidence in favour of one technique over another, there remains the need for further field research to validate the findings. Nevertheless, performance modelling has important implications for the establishment of performance standards and consequent recommendations for training.
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The aim of the present study was to investigate the influence of gear ratio (GR) and thus crank inertial load (CIL), on the activity levels of lower limb muscles. Twelve competitive cyclists performed three randomised trials with their own bicycle equipped with a SRM crankset and mounted on an Axiom ergometer. The power output ( approximately 80% of maximal aerobic power) and the pedalling cadence were kept constant for each subject across all trials but three different GR (low, medium and high) were indirectly obtained for each trial by altering the electromagnetic brake of the ergometer. The low, medium and high GR (mean +/- SD) resulted in CIL of 44 +/- 3.7, 84 +/- 6.5 and 152 +/- 17.9 kg.m(2), respectively. Muscular activity levels of the gluteus maximus (GM), the vastus medialis (VM), the vastus lateralis (VL), the rectus femoris (RF), the medial hamstrings (MHAM), the gastrocnemius (GAS) and the soleus (SOL) muscles were quantified and analysed by mean root mean square (RMS(mean)). The muscular activity levels of the measured lower limb muscles were not significantly affected when the CIL was increased approximately four fold. This suggests that muscular activity levels measured on different cycling ergometers (with different GR and flywheel inertia) can be compared among each other, as they are not influenced by CIL.
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The aims of this study were to examine the cardiovascular response to recreational 5-a-side indoor-soccer (5v5) matches (5v5 study, 5v5S, n=15) and to assess the validity of using heart rate (HR) to estimate oxygen uptake (VO(2)) demands during actual game-play (validity study, VS, n=16) in young subjects (age 16.8+/-1.5 years). Game responses during 5v5S were assessed during 30 min matches using short-range telemetry heart-rate monitors. In VS games (12 min), VO(2) and HR were monitored with a portable gas analyser (K4b(2), COSMED, Rome, Italy). Individual HR-VO(2) relationships were determined from a laboratory treadmill run to exhaustion (VS) and a multistage shuttle running fitness test (5v5S) using K4b(2). Results showed that 5v5 elicits 83.5+/-5.4 and 75.3+/-11.2% of HR(peak) and VO(2peak), respectively. Ninety-one percent of the playing time (30 min) was spent at HR higher than 70% of HR(peak). In VS match, gas analyses revealed that only 71% of HR variance was explained by VO(2) variations. However, playing at approximately 70% of HR(peak) elicited 51.6+/-11.2% of VO(2peak). Group actual versus predicted VO(2) values demonstrated no significant differences (p>0.05), however, large confidence limits were observed (+6.20 and -10.53 mlkg(-1)min(-1)). These results show that HR and VO(2) responses to recreational 5v5 soccer in young athletes are similar to the exercise intensities recommended by ACSM for promoting cardiovascular health and suggest that HR is valid to prescribe and monitor aerobic intermittent exercise. These results also show that HR measures are acceptable for estimating VO(2) during intermittent exercise when assessing large groups, but show that large estimation errors can occur at the individual level.
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Previous researchers have identified significant differences between laboratory and road cycling performances. To establish the ecological validity of laboratory time-trial cycling performances, the causes of such differences should be understood. Hence, the purpose of the present study was to quantify differences between laboratory- and road-based time-trial cycling and to establish to what extent body size [mass (m) and height (h)] may help to explain such differences. Twenty-three male competitive, but non-elite, cyclists completed two 25 mile time-trials, one in the laboratory using an air-braked ergometer (Kingcycle) and the other outdoors on a local road course over relatively flat terrain. Although laboratory speed was a reasonably strong predictor of road speed (R2 = 69.3%), a significant 4% difference (P < 0.001) in cycling speed was identified (laboratory vs. road speed: 40.4 +/- 3.02 vs. 38.7 +/- 3.55 km x h(-1); mean +/- s). When linear regression was used to predict these differences (Diff) in cycling speeds, the following equation was obtained: Diff (km x h(-1)) = 24.9 - 0.0969 x m - 10.7 x h, R2 = 52.1% and the standard deviation of residuals about the fitted regression line = 1.428 (km . h-1). The difference between road and laboratory cycling speeds (km x h(-1)) was found to be minimal for small individuals (mass = 65 kg and height = 1.738 m) but larger riders would appear to benefit from the fixed resistance in the laboratory compared with the progressively increasing drag due to increased body size that would be experienced in the field. This difference was found to be proportional to the cyclists' body surface area that we speculate might be associated with the cyclists' frontal surface area.
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Numerous researchers have studied the physiological responses to seated and standing cycling, but actual field data are sparse. One open issue is the preferred cadence of trained cyclists while hill climbing. The purpose of this study, therefore, was to examine the affect of cycling position on economy and preferred cadence in trained cyclists while they climbed a moderate grade hill at various power outputs. Eight trained cyclists (25.8 ± 7.2 years, $$\ifmmode\expandafter\dot\else\expandafter\.\fi{V}{\text{O}}_{{2\,\max }}$$ 68.8 ± 5.0 ml kg−1 min−1, peak power 407.6 ± 69.0 W) completed a seated and standing hill climb at approximately 50, 65 and 75% of peak power output (PPO) in the order shown, although cycling position was randomized, i.e., half the cyclists stood or remained seat on their first trial at each power output. Cyclists also performed a maximal trial unrestricted by position. Heart rate, power output, and cadence were measured continuously with a power tap; ventilation $$\ifmmode\expandafter\dot\else\expandafter\.\fi{V}{\text{e}}$$, BF and cadence were significantly higher with seated climbing at all intensities; there were no other physiological differences between the climbing positions. These data support the premise that trained cyclists are equally economical using high or low cadences, but may face a limit to benefits gained with increasing cadence.
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The purpose of this study was to examine the acute effect of talocrural joint position on muscle activity and gross mechanical efficiency (GE). Eleven trained cyclists participated in three randomized 6-min cycling bouts at approximately 80% of maximal aerobic capacity on an electromagnetically braked cycle ergometer while oxygen consumption and muscle activity (EMG) were monitored during the subject's self-selected pedaling technique (control) and while using a dorsi- and plantarflexed pedaling technique. The mean differences in range of motion of the dorsi- and plantarflexed technique from the control position were 7.1 +/- 4.4 and 6.9 +/- 5.4 degrees , respectively. Gastrocnemius EMG activity was higher with the dorsiflexion technique than when using the self-selected control position (33.2 +/- 13.0 and 24.2 +/- 8.4 microV s, respectively; P < 0.05). Moreover, GE was 2.6% lower while riding with the dorsiflexion technique than the control position (19.0 +/- 1.2 and 19.5 +/- 1.3%, respectively; P < 0.05). The data suggested that introducing more dorsiflexion into the pedal stroke of a trained cyclist increases muscle activity of the gastrocnemius lateralis and decreased GE when compared to the self-selected pedal stroke.
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The aim of our study was to compare crank torque profile and perceived exertion between the Monark ergometer (818 E) and two outdoor cycling conditions: level ground and uphill road cycling. Seven male cyclists performed seven tests in seated position at different pedaling cadences: (a) in the laboratory at 60, 80, and 100 rpm; (b) on level terrain at 80 and 100 rpm; and (c) on uphill terrain (9.25% grade) at 60 and 80 rpm. The cyclists exercised for 1 min at their maximal aerobic power. The Monark ergometer and the bicycle were equipped with the SRM Training System (Schoberer, Germany) for the measurement of power output (W), torque (Nxm), pedaling cadence (rpm), and cycling velocity (kmxh-1). The most important findings of this study indicate that at maximal aerobic power the crank torque profiles in the Monark ergometer (818 E) were significantly different (especially on dead points of the crank cycle) and generate a higher perceived exertion compared with road cycling conditions.
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The existing literature suggests that crank inertial load has little effect on the responses of untrained cyclists. However, it would be useful to be aware of any possible effect in the trained population, particularly considering the many laboratory-based studies that are conducted using relatively low-inertia ergometers. Ten competitive cyclists (mean VO(2max) = 62.7 ml x kg(-1) x min(-1), s = 6.1) attended the human performance laboratories at the University of Wolverhampton. Each cyclist completed two 7-min trials, at two separate inertial loads, in a counterbalanced order. The inertial loads used were 94.2 kg x m(2) (high-inertia trial) and 2.4 kg x m(2) (low-inertia trial). Several physiological and biomechanical measures were undertaken. There were no differences between inertial loads for mean peak torque, mean minimum torque, oxygen uptake, blood lactate concentration or perceived exertion. Several measures showed intra-individual variability with blood lactate concentration and mean minimum torque, demonstrating coefficients of variation > 10%. However, the results presented here are mostly consistent with previous work in suggesting that crank inertial load has little direct effect on either physiology or propulsion biomechanics during steady-state cycling, at least when cadence is controlled.
Article
Cyclists seek to maximize performance duringcompetition, and gross efficiency is an important factor affectingperformance. Gross efficiency is itself affected by pedal rate. Thus, it is important to understand factors that affect freely chosen pedal rate. Crank inertial load varies greatly during road cycling based on the selected gear ratio. Nevertheless, the possible influence of crank inertial load on freely chosen pedal rate and gross efficiency has never been investigated. This study tested the hypotheses that during cycling with sub-maximal work rates, a considerable increase in crank inertial load would cause (1) freely chosen pedal rate to increase, and as a consequence, (2) gross efficiency to decrease. Furthermore, that it would cause (3) peak crank torque to increase if a constant pedal rate was maintained. Subjects cycled on a treadmill at 150 and 250 W, with low and high crank inertial load, and with preset and freely chosen pedal rate. Freely chosen pedal rate was higher at high compared with low crank inertial load. Notably, the change in crank inertial load affected the freely chosen pedal rate as much as did the 100 W increase in work rate. Alongwith freely chosen pedal rate being higher, gross efficiency at 250 W was lower during cycling with high compared with low crank inertial load. Peak crank torque was higher during cycling at 90 rpm with high compared with low crank inertial load. Possibly, the subjects increased the pedal rate to compensate for the higher peak crank torque accompanying cycling with high compared with low crank inertial
Article
PurposeThe aim of this work was to study the effect of ergometer cycling condition on the electromyographic (EMG) activity of muscles involved in pedalling.
Article
Purpose. – The aim of this work was to study the effect of ergometer cycling condition on the electromyographic (EMG) activity of muscles involved in pedalling. Methods and results. – Seven trained cyclists performed four pedalling trials of three minutes at 70% of maximal aerobic power on a treadmill with a slope of 4% and on an Axiom stationary ergometer Two postures (seated and standing pedalling) were also studied. The global EMG activity of lower limb (sum of the activity of all muscles) is higher when cyclists pedal on Axiom ergometer compared when they cycle on treadmill for the same power output and regardless the body’s posture. Conclusion. – The increase of muscular activity during the stationary ergometer exercise can be due to the lack of lateral and sagital sways of bicycle and also to the constant Axiom brake force which enhanced cyclists to sustain a higher force pedal during each crank cycle.
Article
The critical power (CP) is mathematically defined as the power-asymptote of the hyperbolic relationship between power output and time-to-exhaustion. Physiologically, the CP represents the boundary between the steady-state and nonsteady state exercise intensity domains and therefore may provide a more meaningful index of performance than other well-known landmarks of aerobic fitness such as the lactate threshold and the maximal O2 uptake. Despite the potential importance to sports performance, the CP is often misinterpreted as a purely mathematical construct which lacks physiological meaning and only in recent years has this concept begun to emerge as valid and useful technique for monitoring endurance fitness. This commentary defines the basic principles of the CP concept, outlines its importance to high-intensity exercise performance, and provides an overview of the current methods available for its assessment. Interventions including training, pacing and prior exercise can be used to alter the parameters of the power-time relationship. A future challenge lies in optimizing such interventions in order to positively affect the parameters of the power-time relationship and thereby enhance sports performance in specific events.
Article
The purpose of this study was to describe the relationship between road gradient (RG) and freely chosen cadence (FCC) in a group of professional cyclists during their normal training. In addition, a calculation of crank inertial load (CIL) was estimated in order to establish the relationship between FCC and CIL. Ten professional cyclists were monitored during training using commercially available power meters (Shoberer Rad Messtechnik (SRM), professional version). For each cyclist, recorded training sessions were reviewed to identify the hardest 6-8 training sessions (approximately 18 h of training). RG was estimated based on the relationship between power output, total mass and speed. The analysis was performed using 2113+/-317 samples of 30 s average data, collected on terrain ranging from -4%RG to 12%RG. The individual relationship between FCC and RG could be described by a linear regression model. There was a moderate correlation between FCC and CIL (group's r=0.42), and a multiple regression including the measured power output (WPO) increased the variance explained (R2=0.24). The correlation was very large between CIL and v (r=0.91), and was not strengthened by adding WPO as an independent variable (r=0.91). In conclusion, this investigation documents that in professional cyclists engaged in training, there is a linear decrease in FCC as RG increases (-4%RG and 12%RG). This decrease in FCC appears to be due to the reduction in v as slope increases. It is surmised that CIL plays a key role in the modulation of FCC.
Employing seven male subjects, the influence of four different ergometer flywheels with the moments of inertia at the crankshaft (J') = 5.5, 10.5, 16.5, and 19.5 kg.m2 on 6-min load pulse sum (LPS), the heart rate integrated over 6-min was investigated. The J' was demonstrated to influence LPS at each of the corresponding rotational energies of the flywheels (75, 144, 226 and 276 J at 50 rev.min-1) in the four work-load steps (50, 100, 150 and 200 W). Between the values J' = 5.5 kg.m2 and 10.5 kg.m2 the LPS decreases, to rise again in the range J' = 10.5 kg.m2-19.5 kg.m2. For equal work-loads the minimum LPS was reached at a J' of 10.5 kg.m2. For the workloads of 100, 150 and 200 W it was possible to show statistically significant differences. The moment of inertia of ergometer flywheels J has a smoothing effect on the fluctuations of the rotational speed which are unavoidable during work on a cycle ergometer. The flywheel stores the leg forces acting on the pedals as rotational energy and opposes any rotational acceleration. If the J used is too small, equalization of the fluctuations of the rotational speed remains unsatisfactory. Flywheels with larger J require larger torques at the crankshaft for acceleration. For the most effective delivery of work to a cycle ergometer, an optimal rotational energy of the flywheel was found. For equal physical work, smaller or larger rotational energies require a larger expenditure of biological energy. A J' = 11 +/- 2 kg.m2 was incorporated into the draft for the German standard DIN 13,405 -- cycle-type ergometers.
The purpose of this study was to evaluate the relationships between heart rate (f c), oxygen consumption (VO2), peak force and average force developed at the crank in response to submaximal exercise employing a racing bicycle which was attached to an ergometer (RE), ridden on a treadmill (TC) and ridden on a 400-m track (FC). Eight male trained competitive cyclists rode at three pre-determined work intensities set at a proportion of their maximal oxygen consumption (VO2max): (1) below lactate threshold [work load that produces a (VO2) which is 10% less than the lactate threshold VO2 (sub-LT)], (2) lactate threshold VO2 (LT), and (3) above lactate threshold [workload that produces a VO2 which is 10% greater than lactate threshold VO2 (supra-LT)], and equated across exercise modes on the basis off c. Voltage signals from the crank arm were recorded as FM signals for subsequent representation of peak and average force. Open circuit VO2 measurements were done in the field by Douglas bag gas collection and in the laboratory by automated gas collection and analysis.f c was recorded with a telemeter (Polar Electro Sport Tester, PE3000). Significant differences (P < 0.05) were observed: (1) in VO2 between FC and both laboratory conditions at sub-LT intensity and LT intensities, (2) in peak force between FC and TC at sub-LT intensity, (3) in average force between FC and RE at sub-LT. No significant differences were demonstrated at supra-LT intensity for VO2. Similarly no significant differences were observed in peak and average force for either LT or supra-LT intensities. These data indicate that equating work intensities on the basis off c measured in laboratory conditions would overestimate the VO2 which would be generated in the field and conversely, that usingf c measured in the laboratory to establish field work intensity would underestimate mechanical workload experienced in the field.
Article
Article
The purpose of this study was to test the accuracy of oxygen uptake (VO2) measurements using the Cosmed K4 portable telemetry system. This system of higher technology than the original Cosmed K2 device, contains a CO2 electrode allowing measurements alternatively by either the Cosmed K4 system (K4) or the CPX Medical Graphics (CPX) during a maximum oxygen uptake (VO2max) ergocycle test, at rest and during several submaximal exercises (25, 50 and 75% of maximal work rate) in seven subjects. Heart rate values were comparable for exercise at the same work stage during gas collection using the two systems, indicating that the physiological stresses were similar. The VO2 values did not significantly differ at rest (4.40+/-0.83 vs 4.16+/-0.58ml x min(-1) x kg[-1]), at 25% Wmax (20.97+/-1.31 vs 21.32+/-2.54 ml x min(-1) x kg[-1]), at 50% Wmax (33.32+/-3.92 vs 33.50+/-3.51 ml x min(-1) x kg[-1]), at 75% Wmax (47.01+/-7.51 vs 47.49+/-7.11 ml x min(-1) kg[-1]) and at maximal intensities (62.07+/-8.48 vs 62.84+/-11.31 ml x min(-1) kg[-1]) using K4 and CPX devices, respectively. The results of this study indicated that the K4 system was accurate for all oxygen uptake measurements from rest to maximum exercise levels.
Article
The purpose of this investigation was to assess the accuracy of the COSMED K4 b2 portable metabolic measurement system against the criterion Douglas bag (DB) method. During cycle ergometry on consecutive days, oxygen consumption (VO2), carbon dioxide production (VCO2), minute ventilation (VE), and respiratory exchange ratio (R) were measured at rest and during power outputs of 50, 100, 150, 200, and 250W. No significant differences (P > 0.05) were observed in VO2 between the K4 b2 and DB at rest and at 250W. Though the K4 b2 values were significantly higher (P<0.05) than DB values at 50, 100, 150, and 200 W, the magnitude of these differences was small (0.088, 0.092, 0.096, and 0.088 L x min(-1), respectively). VCO2 and VE values from the K4 b2 were significantly lower than the DB at 200 and 250 W, while no significant differences were observed from rest through 150W. The slight overestimation of VO2 (50-200 W) combined with the underestimation of VCO2 (200 and 250W) by the K4 b2 resulted in significantly lower R values at every stage. These findings suggest the COSMED K4 b2 portable metabolic measurement system is acceptable for measuring oxygen uptake over a fairly wide range of exercise intensities.
Article
This study aimed to assess the accuracy of the Cosmed K4b2 (Cosmed, Italy) portable metabolic system that measures FEO2, FECO2 and V̇E on a breath by breath basis. For gas concentration comparisons, expired air from 20 subjects performing treadmill running was collected in a 600 litre chain compensated Collins Tissot tank and analysed for FEO2 and FECO2 using a laboratory metabolic cart and the Cosmed K4 b2 metabolic system. For ventilation comparisons, serial steady state V̇E (STPD) values were measured on 10 subjects using the Cosmed K4b2 ventilation turbine and a Morgan ventilation monitor during a continuous treadmill running protocol at ascending speeds of 8, 11 and 14 km·h-1. The Cosmed K4b2 FEO2 and FECO2 measures were significantly lower (P<0.001) than the metabolic cart values. Pearson correlation coefficients (r) and the standard error of measurement (SEM) demonstrated a high association between the Cosmed and the metabolic cart measures (FEO2 r= 0.971, SEM 0.071; FECO2 r= 0.925, SEM 0.087). Cosmed V̇E (l·min-1) measures were significantly greater than Morgan values at running speeds of 8 km·h-1 (P<0.001) and 11 km·h-1 (P<0.001) but not significantly different at 14 km·h-1 (P>0.05). When V̇E measures at the three running speeds were combined, the mean difference between instrument measures ranged between 3.5 - 4.0 l·min-1 but the values were highly correlated (r= 0.982, P<0.01; SEM 3.03). Linear regression analysis revealed the following regression equations to predict metabolic cart values from Cosmed measures: FEO2= 0.852+0.963 Cosmed (R2= 0.940, P<0.001), FECO2= 0.627+0.878 Cosmed (R2= 0.856, P<0.001), V̇E= -2.50+0.984 Cosmed (R2= 0.965, P<0.001). The results indicated that the Cosmed K4b2 unit assessed here produced measures of FEO2, FECO2 and V̇E that had strong correlation to values obtained from a metabolic cart. However, linear regression analysis may further improve the accuracy of Cosmed K4b2 measures when compared to metabolic cart values.
Article
Article
The purpose of this study was to assess the validity and reliability of a Cosmed K4b2 portable telemetric gas analysis system. Twelve physically fit males performed a treadmill running session consisting of an easy 10 min run, a hard 3 min run and a 1 min sprint (with rest periods of 10 min separating each run), on four separate occasions. Sessions were identical with the exception of the apparatus used to measure VO2. During two (test-retest) sessions a Cosmed K4b2 portable gas analysis system was used; in another, a laboratory metabolic cart and, in one session, both systems were used to measure VO2 simultaneously. Comparison of Cosmed K4b2 and metabolic cart measurements in isolation revealed significantly (p < 0.05) increased values of VO2, VCO2, FE CO2 (except FE CO2 at 10 min) and lower values of FE O2 for each run duration by the Cosmed system. Linear regression equations to predict metabolic cart results from Cosmed values were, respectively; cart VO2 = 0.926 (Cosmed VO2-0.227 (r2 = 0.84) and cart VCO2 = 1.057 (Cosmed VCO2-0.606 (r2 = 0.92). Bland-Altman plots and comparison of the test-retest cosmed measurements revealed that the K4b2 system showed good repeatability of measurement for measures of VE, VO2 and VCO2, particularly for 10 min and 3 min tests (ICC = 0.7-0.9, p < 0.05). In conclusion, the Cosmed K4b2 portable gas analysis system recorded consistently higher VO2 and VCO2 measurements in comparison to a metabolic cart. However, satisfactory test-retest reliability of the system was demonstrated.
Article
The aim of this experiment was to compare the efficiency of elite cyclists with that of trained and recreational cyclists. Male subjects (N = 69) performed an incremental exercise test to exhaustion on an electrically braked cycle ergometer. Cadence was maintained between 80 - 90 rpm. Energy expenditure was estimated from measures of oxygen uptake (VO (2)) and carbon dioxide production (VCO(2)) using stoichiometric equations. Subjects (age 26 +/- 7 yr, body mass 74.0 +/- 6.3 kg, Wpeak 359 +/- 40 W and VO(2)peak 62.3 +/- 7.0 mL/kg/min) were divided into 3 groups on the basis of their VO (2)peak (< 60.0 (Low, N = 26), 60 - 70 (Med, N = 27) and > 70 (High, N = 16) mL/kg/min). All data are mean +/- SE. Despite the wide range in aerobic capacities gross efficiency (GE) at 165 W (GE (165)), GE at the same relative intensity (GE (final)), delta efficiency (DE) and economy (EC) were similar between all groups. Mean GE (165) was 18.6 +/- 0.3 %, 18.8 +/- 0.4 % and 17.9 +/- 0.3 % while mean DE was 22.4 +/- 0.4 %, 21.6 +/- 0.4 % and 21.2 +/- 0.5 % (for Low, Medium and High, respectively). There was no correlation between GE (165), GE (final), DE or EC and VO(2)peak. Based on these data, we conclude that there are no differences in efficiency and economy between elite cyclists and recreational level cyclists.
Article
While sprint track running events, lasting 10-25 secs, are characterised by an anaerobic metabolic dominance, no actual track running data exist which have quantified the relative energy system contributions. Using previous methods employed by our laboratory, including 'in race' measures of VO2, accumulated oxygen deficit (AOD), blood lactate concentration and estimated phosphocreatine degradation (La/PCr), the aerobic-anaerobic energy system contributions to 100-m and 200-m events were calculated. For the 100-m event, results indicated a relative aerobic-anaerobic energy system contribution (based on AOD measures) of 21%-79% and 25-75% for males and females respectively (9%-91% and 11%-89% based on La/PCr measures; p<0.05 for both genders for 100-m from AOD estimates). For the 200-m, a 28%-72% and 33%-67% contribution for male and female athletes was estimated (21%-79% and 22%-78% based on La/PCr measures; NS from AOD estimates). A range of energy system contribution estimates for events of these durations have previously been proposed using a variety of techniques. The data from the current study also show different results depending on the measurement technique utilised. While AOD measures are often used to estimate anaerobic energy contribution, at such high exercise intensities (and brief exercise durations) as used in the present study, AOD measures showed larger aerobic energy estimates than expected.
Article
The purpose of this study was to determine the validity and the reliability of a stationary electromagnetically-braked cycle ergometer (Axiom PowerTrain) against the SRM power measuring crankset. Nineteen male competitive cyclists completed four tests on their bicycle equipped with a 20-strain gauges SRM crankset: a maximal aerobic power (MAP) test and three 10-min time trials (TTs) with three different simulated slopes (0, 3, and 6 %). The Axiom ergometer overestimated (p <0.05) the SRM power output during the last stage of the MAP test and during TTs, by 5 % and 12 %, respectively. Power output (PO) of the Axiom ergometer drifted significantly (p <0.05) with the time during TT. These findings indicate that the Axiom ergometer does not provide a valid measure of PO compared with SRM. However, the small coefficient of variation (2.2 %) during the MAP test indicates that the Axiom provides a reliable PO and that it can be used e.g. for relative PO comparisons with competitive cyclists during a race season.
Article
Despite the importance of uphill cycling performance during cycling competitions, there is very little research investigating uphill cycling, particularly concerning field studies. The lack of research is partly due to the difficulties in obtaining data in the field. The aim of this study was to analyse the crank torque in road cycling on level and uphill using different pedalling cadences in the seated position. Seven male cyclists performed four tests in the seated position (1) on level ground at 80 and 100 rpm, and (2) on uphill road cycling (9.25% grade) at 60 and 80 rpm.The cyclists exercised for 1 min at their maximal aerobic power. The bicycle was equipped with the SRM Training System (Schoberer, Germany) for the measurement of power output (W), torque (Nm), pedalling cadence (rpm), and cycling velocity (km h(-1)). The most important finding of this study indicated that at maximal aerobic power the crank torque profile (relationship between torque and crank angle) varied substantially according to the pedalling cadence and with a minor effect according to the terrain. At the same power output and pedalling cadence (80 rpm) the torque at a 45 degrees crank angle tended (p < 0.06) to be higher (+26%) during uphill cycling compared to level cycling. During uphill cycling at 60 rpm the peak torque was increased by 42% compared with level ground cycling at 100 rpm. When the pedalling cadence was modified, most of the variations in the crank torque profile were localised in the power output sector (45 degrees to 135 degrees).
Article
The aim of this study was to compare the maximal power output (POpeak) and force-velocity relationships in sprint cycling obtained from a laboratory protocol and from a field test during actual cycling locomotion. Seven male competitive cyclists performed 6 sprints (3 in the seated position and 3 in the standing position) on an ergo-trainer (Tacx, Netherlands) and 6 sprints during actual cycling locomotion in a gymnasium. The bicycle was equipped with the SRM Training System (Schoberer Rad Messtechnik, Germany) to measure (200 Hz) the power output (PO, W), the pedalling cadence (rpm), and the velocity (kmxh-1). From these measurements, the maximal force on the pedal (Fmax), the theoretical maximal force (Fo, N) and the theoretical maximal pedalling cadence (V0, rpm) were determined. During each sprint test the lateral bicycle oscillations were measured from a video analysis. During standing and seated sprints in the gymnasium, Fo and Fmax were significantly higher (p<0.05) compared with sprints on the ergo-trainer (+12% and +32%, respectively). The POpeak during sprints in seated and standing positions in the gymnasium was significantly (p<0.05) lower (-4%) and higher (+6%) respectively, compared with the ergo-trainer. For standing position in the gymnasium the kinematics analysis indicated a 24 degrees mean lateral bicycle oscillation compared with 0 degrees on the ergo trainer. The results of this study indicate that POpeak, Fo, and time to obtain POpeak were different between laboratory and actual cycling conditions. To obtain a valid estimation of the maximal power output, it is necessary to perform sprint tests during actual cycling locomotion. Thus, in the laboratory, it is advisable to use a cycle ergometer that enables natural lateral oscillations.
Article
The aim of the present study was to determine the time sustained near VO2max in two interval training (IT) swimming sessions comprising 4x400 m (IT(4x400)) or 16x100 (IT(16xl00)). Elite swimmers (Mean+/-SD age 18+/-2 yrs; body mass 66.9+/-6.5 kg: swim VO2max 55.7+/-5.8 ml.kg(-1).min(-1)) completed three experimental sessions at a 50-m indoor pool over a one week period. The first test comprised a 5 x 200-m incremental test to exhaustion for determination of the pulmonary ventilation threshold (VT, m.s(-1)), VO2max, the velocity associated with VO2max (VO2max, m(s(-1)) and maximum heart rate (HR(max), b.min(-1)). The remaining two tests involved the IT(4x400) and IT(16xl00) performed in a randomised order. The two IT sessions where completed at a velocity representing 25% of the difference between the VT and the VO2max (delta25%) and in the same work to rest ratio. During the IT sessions VO2 as well as HR were measured. The duration (s) >90% VO2max, also the duration (s) >90% HR(max), were not significantly different in the IT(16x100) and IT(4x400). However, limits of agreement (LIM(AG)) analysis demonstrated considerable individual variation in the time >90% VO2max (mean difference +/-2SD = 222+/-819 s) and the time >90% HRmax (mean difference +/-2SD = 61+/-758 s) between the two IT sessions. This factor deserves further research to establish the characteristics of those athletes which influence the physiological responses in IT of short or longer duration repetitions.
Article
This study was conducted to determine the effect of high pedaling cadences on maximal cycling power output (W(max)). Nine well-trained cyclists performed a continuous, incremental cycle-ergometer test to exhaustion (25 W increases every 3 min) either at 80, 100, or 120 rpm on three different occasions. W(max) was approximately 9% lower during 120 rpm in comparison with 80 and 100 rpm (335 +/- 9, 363 +/- 7, and 370 +/- 12 W, respectively; P < 0.05). During 120 rpm, ventilation rate (V(E)) increased above the increases in expired CO(2), which reduced the power output (PO) at the ventilatory anaerobic threshold (VT(2)) by 11% (P < 0.05). Gross efficiency (GE) did not differ among trials. At 120 rpm, capillary blood lactate concentration ([Lac]) increased above the 80-rpm trial (5.3 +/- 1.2 vs 3.0 +/- 0.7 mM at 300 W; P < 0.05), although pH was not reduced. At 120 rpm, expired CO(2) increased and reduced blood bicarbonate concentration ([HCO(3)(-)]) was reduced, maintaining blood pH similar to the other trials. A high pedaling cadence (i.e., 120 rpm) reduces performance (i.e., W(max)) and anaerobic threshold during an incremental test in well-trained cyclists. The data suggest that ventilatory anaerobic threshold (VT(2)) is a sensitive predictor of optimal pedaling cadence for performance, whereas blood pH or efficiency is not.
Article
Despite the wide use of surface electromyography (EMG) to study pedalling movement, there is a paucity of data concerning the muscular activity during uphill cycling, notably in standing posture. The aim of this study was to investigate the muscular activity of eight lower limb muscles and four upper limb muscles across various laboratory pedalling exercises which simulated uphill cycling conditions. Ten trained cyclists rode at 80% of their maximal aerobic power on an inclined motorised treadmill (4%, 7% and 10%) with using two pedalling postures (seated and standing). Two additional rides were made in standing at 4% slope to test the effect of the change of the hand grip position (from brake levers to the drops of the handlebar), and the influence of the lateral sways of the bicycle. For this last goal, the bicycle was fixed on a stationary ergometer to prevent the lean of the bicycle side-to-side. EMG was recorded from M. gluteus maximus (GM), M. vastus medialis (VM), M. rectus femoris (RF), M. biceps femoris (BF), M. semimembranosus (SM), M. gastrocnemius medialis (GAS), M. soleus (SOL), M. tibialis anterior (TA), M. biceps brachii (BB), M. triceps brachii (TB), M. rectus abdominis (RA) and M. erector spinae (ES). Unlike the slope, the change of pedalling posture in uphill cycling had a significant effect on the EMG activity, except for the three muscles crossing the ankle's joint (GAS, SOL and TA). Intensity and duration of GM, VM, RF, BF, BB, TA, RA and ES activity were greater in standing while SM activity showed a slight decrease. In standing, global activity of upper limb was higher when the hand grip position was changed from brake level to the drops, but lower when the lateral sways of the bicycle were constrained. These results seem to be related to (1) the increase of the peak pedal force, (2) the change of the hip and knee joint moments, (3) the need to stabilize pelvic in reference with removing the saddle support, and (4) the shift of the mass centre forward.
Article
Performance models provide an opportunity to examine cycling in a broad parameter space. Variables used to drive such models have traditionally been measured in the laboratory. The assumption, however, that maximal laboratory power is similar to field power has received limited attention. The purpose of the study was to compare the maximal torque- and power-pedaling rate relationships during "all-out" sprints performed on laboratory ergometers and on moving bicycles with elite cyclists. Over a 3-day period, seven male (mean +/- SD; 180.0 +/- 3.0 cm; 86.2 +/- 6.1 kg) elite track cyclists completed two maximal 6 s cycle ergometer trials and two 65 m sprints on a moving bicycle; calibrated SRM powermeters were used and data were analyzed per revolution to establish torque- and power-pedaling rate relationships, maximum power, maximum torque and maximum pedaling rate. The inertial load of our laboratory test was (37.16 +/- 0.37 kg m(2)), approximately half as large as the field trials (69.7 +/- 3.8 kg m(2)). There were no statistically significant differences between laboratory and field maximum power (1791 +/- 169; 1792 +/- 156 W; P = 0.863), optimal pedaling rate (128 +/- 7; 129 +/- 9 rpm; P = 0.863), torque-pedaling rate linear regression slope (-1.040 +/- 0.09; -1.035 +/- 0.10; P = 0.891) and maximum torque (266 +/- 20; 266 +/- 13 Nm; P = 0.840), respectively. Similar torque- and power-pedaling rate relationships were demonstrated in laboratory and field settings. The findings suggest that maximal laboratory data may provide an accurate means of modeling cycling performance.
Original Research: Gross efficiency and cycling economy are higher in the field as compared to on an Axiom stationary ergometer
"Original Research: Gross efficiency and cycling economy are higher in the field as compared to on an Axiom stationary ergometer" by Bertucci WM et al Journal of Applied Biomechanics © 2012 Human Kinetics, Inc.
Level ground and uphill cycling efficiency in seated and standing positions
• G P Millet
• C Tronche
• N Fuster
• R Candau
Millet, G.P., Tronche, C., Fuster, N., & Candau, R. (2002). Level ground and uphill cycling efficiency in seated and standing positions. Medicine and Science in Sports and Exercise, 34(10), 1645-1652. PubMed doi:10.1097/00005768-200210000-00017
Response of ventilatory and lactate thresholds to continuous and interval training
• D Poole
• G Gaesser
• M J Pubmed Quod
• D T Martin
• J C Martin
• P B Laursen
Poole, D., & Gaesser, G. (1985). Response of ventilatory and lactate thresholds to continuous and interval training. Journal of Applied Physiology, 58, 1115-1121. PubMed Quod, M.J., Martin, D.T., Martin, J.C., & Laursen, P.B. (2010). The Power Profile Predicts Road Cycling MMP. International Journal of Sports Medicine, 31, 397-401. PubMed doi:10.1055/s-0030-1247528
• Millet G.P.