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Experimental set-up for the Velotron cycle ergometer evaluation. An SRM crank is mounted to the Velotron and a fi rst principles dynamic calibration rig is used to apply torque to the system. Power during all trials was recorded on 1) a computer interfaced to the calibration rig, 2) a computer interfaced to the Velotron and 3) a data logger connected to the SRM crank.
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... meter and 3) a Velotron cycle ergometer. All trials were conducted in standard laboratory conditions (16 -22 ° C and 40 -70 % relative humidity). During the trials, the SRM power meter was mounted to the Velotron cycle ergometer; then during all trials other than the dynamic time trial, the dynamic calibration rig was attached as depicted in Fig. 1 ...
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... The accuracy of Velotron is described elsewhere. 18 ...
Purpose: Functional Threshold Power (FTP), determined as 95% of the average power during a 20-minute time-trial test, is suggested as a practical test for the determination of the maximal lactate steady state (MLSS) in cycling. Therefore, the objective of the present study was to determine the validity of FTP in predicting MLSS.
Method: Fifteen cyclists, 7 classified as trained and 8 as well-trained (mean ± standard deviation; maximal oxygen uptake = 62.3 ± 6.4 mL/kg/min, maximal aerobic power = 329 ± 30 Watts), performed an incremental test to exhaustion, an FTP test, and several constant load tests to determine the MLSS. The bias ± 95% limits of agreement (LoA), typical error of the estimate (TEE), and Pearson´s coefficient of correlation (r) were calculated to assess validity.
Results: For the power output measures, FTP presented a bias ± 95% LoA of 1.4 ± 9.2%, a moderate TEE (4.7%), and nearly perfect correlation (r = 0.91) with MLSS in all cyclists together. When divided by the training level, the bias ± 95% LoA and TEE were higher in the trained group (1.4 ± 11.8% and 6.4%, respectively) than in the well-trained group (1.3 ± 7.4% and 3.0%, respectively). For the heart rate measurement, FTP presented a bias ± 95% LoA of −1.4 ± 8.2%, TEE of 4.0%, and very -large correlation (r = 0.80) with MLSS.
Conclusion: Therefore, trained and well-trained cyclists can use FTP as a noninvasive and practical alternative to estimate MLSS
... A number of cycle ergometers have been developed and assessed including the Velotron Dynafit Pro cycle ergometer (RacerMate Inc, WA, USA). The Velotron is a reliable and valid ergometer that was compared over a range of exercise intensities and durations with the calibration rig [11]. Therefore, the Velotron may be used as a valid criterion to evaluate PO during GXT and cycling performance during TT [11][12][13]. ...
... The Velotron is a reliable and valid ergometer that was compared over a range of exercise intensities and durations with the calibration rig [11]. Therefore, the Velotron may be used as a valid criterion to evaluate PO during GXT and cycling performance during TT [11][12][13]. In this regard, the aim of this study was to analyze the validity of the Stages mountain biking power meter with a cycling GXT. ...
... All physiological and performance assessments were completed on a Velotron Dynafit Pro (RacerMate Inc, WA, USA) cycle ergometer calibrated in accordance with the manufacturer's instructions. The Velotron ergometer has previously been shown to provide valid and reliable power measurements during steady state output in comparison to a gold standard calibration rig [11]. Prior to testing each participant was fitted to the ergometer in a position to replicate his own racing bicycle. ...
BACKGROUND: Stages device is an affordable power meter used by several professional and competitive cyclists. However, few studies have investigated its validity and reliability. OBJECTIVE: The aim of this study was to analyze the validity and the reliability of the Stages mountain biking power meter. METHODS: Twenty-six cyclists (age: 35 ± 7 years, VO 2 max: 58 ± 7 ml⋅ kg - 1 ⋅ min - 1 ) completed six testing sessions within a 4-week period. Cyclists performed an incremental exercise test, a 5-min time trial (TT), and a 20-min TT in different days using the Velotron cycle ergometer attached with the power meter. RESULTS: The power output of the Stages was significantly lower during each step of the incremental exercise test (11.8 to 16.4%) and performance testing (∼ 18%) compared with the cycle ergometer. Bland Altman plots presented high bias and limits of agreements when comparing the power output of the Stages and Velotron. In general, high coefficient of variation was found for the power and cadence of the Velotron and Stages, except for the Velotron’s power during the 20-min TT. CONCLUSIONS: The interchangeable use of the power from the Velotron and Stages is not recommended due to the observed high within-subject variation.
... Due to the importance of recording power with an appropriate level of accuracy and reliability for the power meters' intended use, a number of studies have been conducted to establish the level of accuracy and reliability of commercially available cycling power meters such as the SRM (Abbiss, Quod, Levin, Martin, & Laursen, 2009;Gardner et al., 2004;Jones & Passfield, 1998;Lawton, Martin, & Lee, 1999), PowerTap® (Bertucci, Duc, Villerius, Pernin, & Grappe, 2005;Gardner et al., 2004), Ergomo Pro ( Duc et al., 2007;Kirkland, Coleman, Wiles, & Hopker, 2008), Look Keo (Sparks, Dove, Bridge, Midgely, & McNaughton, 2014), Polar® S710 (Millet, Tronche, Fuster, Bentley, & Candau, 2003), G-Cog (Bertucci, Crequy, & Chiementin, 2013), and power measuring cycle ergometers such as the Kingcyle (Balmer, Davison, Coleman, & Bird, 2000), Axiom Powertrain ), Velotron ( Abbiss et al., 2009), Wattbike (Hopker et al., 2010) and a new design of ergometer (Bertucci, Grappe, & Crequy, 2011). ...
... Due to the importance of recording power with an appropriate level of accuracy and reliability for the power meters' intended use, a number of studies have been conducted to establish the level of accuracy and reliability of commercially available cycling power meters such as the SRM (Abbiss, Quod, Levin, Martin, & Laursen, 2009;Gardner et al., 2004;Jones & Passfield, 1998;Lawton, Martin, & Lee, 1999), PowerTap® (Bertucci, Duc, Villerius, Pernin, & Grappe, 2005;Gardner et al., 2004), Ergomo Pro ( Duc et al., 2007;Kirkland, Coleman, Wiles, & Hopker, 2008), Look Keo (Sparks, Dove, Bridge, Midgely, & McNaughton, 2014), Polar® S710 (Millet, Tronche, Fuster, Bentley, & Candau, 2003), G-Cog (Bertucci, Crequy, & Chiementin, 2013), and power measuring cycle ergometers such as the Kingcyle (Balmer, Davison, Coleman, & Bird, 2000), Axiom Powertrain ), Velotron ( Abbiss et al., 2009), Wattbike (Hopker et al., 2010) and a new design of ergometer (Bertucci, Grappe, & Crequy, 2011). ...
... Although in many studies the SRM powermeter has been used as the criterion measure, an alternative, appropriate method reported in the literature to assess the validity of power measurement systems and ergometers is through the use of motorised calibration rigs ( Abbiss et al., 2009;Lawton et al., 1999;Maxwell et al., 1998;Jones & Passfield, 1998;Russell & Dale, 1986;Wilmore et al., 1982). Although various designs have been employed, the most common type of motorised calibration rig used now incorporates a speed-controlled motor to apply a torque to the bicycle pedal or bottom bracket via a crankshaft. ...
The purpose of the study was to assess the validity and inter-bike reliability of 10 Wattbike cycle ergometers, and to assess the test-retest reliability of one Wattbike. Power outputs from 100 to 1000 W were applied using a motorised calibration rig (LODE) at cadences of 70, 90, 110 and 130 rev · min(-1), which created nineteen different intensities for comparison. Significant relationships (P < 0.01, r(2) = 0.99) were found between each of the Wattbikes and the LODE. Each Wattbike was found to be valid and reliable and had good inter-bike agreement. Within-bike mean differences ranged from 0.0 W to 8.1 W at 300 W and 3.3 W to 19.3 W at 600 W. When taking into account the manufacturers stated measurement error for the LODE (2%), the mean differences were less than 2%. Comparisons between Wattbikes at each of the nineteen intensities gave differences from 0.6 to 25.5 W at intensities of 152 W and 983 W, respectively. There was no significant difference (P > 0.05) between the measures of power recorded in the test-retest condition. The data suggest that the Wattbike is an accurate and reliable tool for training and performance assessments, with data between Wattbikes being able to be used interchangeably.
... The validity of the power meter can be high where power output is measured directly and calculated from its derivatives, angular velocity multiplied by torque Abbiss et al. (2009). For example, at the rear hub angular velocity is calculated from wheel rotation, and torque from the force transmitted by the chain to the hub. ...
Mobile power meters provide a valid means of measuring cyclists’ power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist’s training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session’s variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists’ modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.
... The time taken to complete the 2.5-km was then calculated based upon the average velocity performed during the 2.5-km. The power output recorded by the Velotron cycle ergometer has previously been reported to be highly accurate and valid ( < 1 % error) when compared to a dynamic calibration rig [5] and an SRM power monitor [5,22] . Throughout the trials, skin temperature (T sk ) was measured using four fl at-top copper skin thermistors (YTS Temperature, 400 Series; Dayton, OH) attached to the chest, arm, thigh and calf [20,29,34] and mean skin temperature was determined by Ramanathan ' s formula: [13] Rectal temperature (T re ) was determined using a disposable rectal thermistor (Monatherm Thermistor, 400 Series; Mallinckrodt Medical, St. Louis, MO, USA) which was self-inserted to a depth of 12 cm from the sphincter. ...
... The time taken to complete the 2.5-km was then calculated based upon the average velocity performed during the 2.5-km. The power output recorded by the Velotron cycle ergometer has previously been reported to be highly accurate and valid ( < 1 % error) when compared to a dynamic calibration rig [5] and an SRM power monitor [5,22] . Throughout the trials, skin temperature (T sk ) was measured using four fl at-top copper skin thermistors (YTS Temperature, 400 Series; Dayton, OH) attached to the chest, arm, thigh and calf [20,29,34] and mean skin temperature was determined by Ramanathan ' s formula: [13] Rectal temperature (T re ) was determined using a disposable rectal thermistor (Monatherm Thermistor, 400 Series; Mallinckrodt Medical, St. Louis, MO, USA) which was self-inserted to a depth of 12 cm from the sphincter. ...
... It is also possible that the relatively slow performance times observed in this study may have been exacerbated by an inability of the Velotron cycle ergometer to accurately calculate realistic cycling speeds. While the accuracy and reliability of the Velotron to determine cycling power output has previously been determined [5,36] , no study to date has attempted to examine the accuracy and validity of cycling speed calculated by the Velotron software. Indeed, by comparing the present study to that of previous research [9,15] it appears that the Velotron cycle ergometer underestimates velocity at a given cycling power output. ...
The purpose of this study was to determine the influence of starting strategy on time trial performance in the heat. Eleven endurance trained male cyclists (30+/-5 years, 79.5+/-4.6 kg, VO(2max) 58.5+/-5.0 ml x kg x (-1) min(-1)) performed four 20-km time trials in the heat (32.7+/-0.7 degrees C and 55% relative humidity). The first time trial was completed at a self-selected pace (SPTT). During the following time trials, subjects performed the initial 2.5-km at power outputs 10% above (10% ATT), 10% below (10% BTT) or equal (ETT) to that of the average power during the initial 2.5-km of the self-selected trial; the remaining 17.5-km was self-paced. Throughout each time trial, power output, rectal temperature, skin temperature, heat storage, pain intensity and thermal sensation were taken. Despite significantly (P<0.05) greater power outputs for 10% BTT (273+/-45W) compared with the ETT (267+/-48W) and 10% ATT (265+/-41W) during the final 17.5-km, overall 20-km performance time was not significantly different amongst trials. There were no differences in any of the other measured variables between trials. These data show that varying starting power by +/-10% did not affect 20 km time trial performance in the heat.
Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.
PGC-1α mRNA is increased with both exercise and exposure to cold temperature. However, transcriptional control has yet to be examined during exercise in the cold. Additionally, the need for environmental cold exposure after exercise may not be a practical recovery modality. The purpose of this study was to determine mitochondrial-related gene expression and transcriptional control of PGC-1α following exercise in a cold compared to room temperature environment. Eleven recreationally trained males completed two 1-h cycling bouts in a cold (7 {degree sign}C) or room temperature (20 {degree sign}C) environment followed by 3 h of supine recovery in standard room conditions. Muscle biopsies were taken from the vastus lateralis pre-exercise, post-exercise, and after 3-h recovery. Gene expression and transcription factor binding to the PGC-1α promoter was analyzed. PGC-1α mRNA increased from pre-exercise to 3-h recovery, but there was no difference between trials. ERRα, MEF2, and NRF2 mRNA was lower in cold than room temperature. FOXO1 and CREB binding to the PGC-1α promoter were increased post-exercise and at 3-h recovery. MEF2 binding increased post-exercise and ATF2 binding increased at 3-h recovery. These data indicate no difference in PGC-1α mRNA or transcriptional control after exercise in cold vs. room temperature and 3-h recovery. However, the observed reductions in the mRNA of select transcription factors downstream of PGC-1α indicate a potential influence of exercise in the cold on the transcriptional response related to mitochondrial biogenesis.
