SRM Powermeter Research Articles

Caveats and Recommendations to Assess the Validity and Reliability of Cycling Power Meters: A Systematic Scoping Review.

A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test).

Power Analysis of Field-Based Bicycle Motor Cross (BMX).

IntroductionPower meter is a useful tool for monitoring cyclists’ training and race performance. However, limited data are available regarding BMX racing power output. The aim of this study was to characterise the power production of BMX riders and investigate its potential role on race performance.MethodsFourteen male riders (age: 20.3 ± 1.5 years, height: 1.75 ± 0.05 m, mass: 70.2 ± 6.4 kg) participated in this study. The tests consist of performing two races apart from 15-min recovery. SRM power meter was used to record power and cadence. Cyclists’ fastest race was used for the data analysis. Heart rate was recorded at 1-s intervals using a Garmin HR chest strap. Lap time was recorded using four pairs of photocells positioned at the start gate, bottom of the start ramp, end of first corner (time cornering), and on the finish line.ResultsThere was a large correlation between race time and relative peak power (r = −0.68, p < 0.01) as well as average power with zero value excluded (r = −0.52, p < 0.01). Race time was also significantly associated with time cornering (r = 0.58, p < 0.01). Peak power (1288.7 ± 62.6 W) was reached in the first 2.34 second of the race. With zero values included, the average power was 355.8 ± 25.4 W, which was about 28% of the peak power, compared to 62% when zero values were excluded (795.6 ± 63.5 W).ConclusionThe post-race analysis of the power data might help the cyclists recognizing the need to apply certain strategies on pedalling rates and power production in certain portions of the BMX track, specially, at the start and around the first corner. BMX coaches must consider designing training programs based on the race intensity and power output zones.

Validity and reliability of the CycleOps Hammer direct drive trainer during sprint tests when compared with an SRM powermeter – a preliminary study

Background: Coaches and cyclists often use direct drive home-trainers for measuring power output (PO) during testing and training. Several devices like the Tacx Fortius (Peiffer et al., 2011: International Journal of Sports Medicine, 32, 353-356) and the Velotron trainer (Abbiss et al., 2009: International Journal of Sports Medicine, 30, 107-112) have been scientifically validated when compared to an SRM powermeter which is considered as the gold standard (Gardner et al., 2004: Medicine Science in Sports and Exercise, 36, 1252-1258). Contrarywise, others such as the Axiom Elite (Bertucci et al., 2005: International Journal of Sports Medicine, 26, 59-65) or the Kickr Power trainer (Zadow et al., 2016: International Journal of Sport Physiology and Performance, 11, 1115-1117) are less accurate and reliable. A recent study reported that the CycleOps Hammer provides valid and reliable PO measurements during graded exercise tests (100-500W). This validation procedure is nevertheless uncompleted as no comparison in maximal PO during all-out sprints have been done.  Purpose: The purpose of this study was to assess the validity and reliability of PO measurement of three Cycleops Hammer trainer devices during maximal sprint tests when compared to an SRM powermeter.  Methods: Three cyclists voluntarily participated in this study (Mean ± SD: 28 ± 9 years old; 1.81 ± 0.02 m; 72 ± 5 kg). Each subject performed three one-day laboratory test sessions separated by at least 48 h, that comprised successively: 1) one maximal graded exercise test (100-425 W) performed at a constant pedalling cadence (~90 rpm) ; 2) three 10-s all-out sprint tests against three different slope resistance (0.5%, 3%, 7%) ; 3) one 30-s all-out Wingate test (slope resistance of 3 %) ; and 4) a 20 min constant power test to study the potential power drift. All the tests were performed on the same road bike (Roubaix Tarmac, Specialized, USA) that was mounted on to a Hammer direct drive trainer (CycleOps, Madison, USA) and fitted with a SRM professional powermeter (SRM FSA K-Force Light, SRM GmbH, JA¼lich, Germany; accuracy ± 1 %). Prior to the study, the slope of the SRM powermeter was calibrated with set of weights. The zero offset of each device was performed according to the manufacturer’s recommendations before each test session. Power output (PO) and pedalling cadence (PC) were measured continuously by the two devices every second and stored in a GPS bike computer (Edge 520, Garmin, USA). The PO differences between the two devices was assessed by non-parametric Wilcoxon tests. To study the accuracy of each Hammer trainer, the relative bias of the PO differences between the SRM and the Hammer trainer and its 95% limits of agreement was defined using the Bland-Altman method. The 95% confidence interval (95%CI) for the relative bias was also calculated. All significant differences were set at p < 0.05. Only the data obtained during sprints and Wingate tests are presented in this abstract.  Results:  Significant differences in 1-s peak PO (sprints) and in 30-s mean PO (Wingate) were observed between the three Hammer devices and the SRM powermeter. When compared to the SRM powermeter, mean ± SD of bias for 1-s peak PO was 11.3 ± 10.3 % for Hammer 1, 13.2 ± 12.7% for Hammer 2 and 21.1 ± 9.5% for Hammer 3. Mean ± SD of bias for 30-s mean PO was 4.3 ± 3.0% for Hammer 1, 1.7 ± 2.4% for Hammer 2 and 5.5 ± 1.0% for Hammer 3. No significant differences were found in maximal (sprint) and mean pedalling cadence (Wingate) whatever the Hammer device used. Discussion and conclusion: These preliminary results seems to suggest that the CycleOps Hammer is not a valid device to measure PO with a high accuracy during all-out exercises as sprints. Nevertheless, according the lowest bias observed in 30-s mean PO during Wingate test, the overestimation of PO tends to decrease with increasing averaging time. Future studies should confirm it by increasing the sample size.

Comparison of Oxygen Uptake Kinetics During Severe-intensity Laboratory and Field Cycling

The O2-kinetic response to constant work rate exercise provides an insight into the adjustment of systemic oxygen transport and muscle metabolism. Whether O2-kinetics measured in laboratory conditions reflect O2-kinetics in field conditions has not yet been analysed. The aim of this study was to compare O2-kinetics between field and laboratory conditions. Thirteen competitive male cyclists (mean±SD age 23.3±4.1 years; V̇O2peak 68.2±4.7 mL.min-1.kg-1) completed two 6-min severe-intensity trials at 60 and 90 rev.min-1 in both conditions. Power output was measured with an SRM power meter and V̇O2 was measured with a portable gas analyser. The time constant (τ), the time delay (TD) and the amplitude (Amp) were resolved by least square regression, and the V̇O2 slow component (SC) was calculated as the difference between the end-exercise V̇O2 and Amp. To determine differences between the trials, a repeated-measure ANOVA was conducted. The Amp and end-exercise V̇O2 were significantly higher during field cycling whereas the SC were significantly higher during laboratory cycling (all at p<0.001). No significant differences were found for τ (p=0.24). Laboratory measures tend to underestimate the oxygen demand in field cycling. A higher cadence leads to greater oxygen demand in laboratory and field cycling.

Validity and Reliability of the Stages Cycling Power Meter.

Granier, C, Hausswirth, C, Dorel, S, and Le Meur, Y. Validity and reliability of the stages cycling power meter. J Strength Cond Res 34(12): 3554-3559, 2020-This study aimed to determine the validity and the reliability of the Stages power meter crank system (Boulder, United States) during several laboratory cycling tasks. Eleven trained subjects completed laboratory cycling trials on an indoor cycle fitted with SRM Professional and Stages systems. The trials consisted of an incremental test at 100 W, 200 W, 300 W, 400 W, and four 7-s sprints. The level of pedaling asymmetry was determined for each cycling intensity during a similar protocol completed on a Lode Excalibur Sport ergometer. The reliability of Stages and SRM power meters was compared by repeating the incremental test during a test-retest protocol on a Cyclus 2 ergometer. Over power ranges of 100-1,250 W, the Stages system produced trivial to small differences compared with the SRM (standardized typical error values of 0.06, 0.24, and 0.08 for the incremental, sprint, and combined trials, respectively). A large correlation was reported between the difference in power output (PO) between the 2 systems and the level of pedaling asymmetry (r = 0.58, p < 0.001). Recalculating PO of the Stages system according to the level of pedaling asymmetry provided only marginal improvements in PO measures. The reliability of the Stages power meter at the submaximal intensities was similar to the SRM Professional model (coefficient of variation: 2.1 and 1.3% for Stages and SRM, respectively). The Stages system is a suitable device for PO measurements, except when a typical error of measurement <3.0% over power ranges of 100-1,250 W is expected.

Validity and Reliability of the Garmin Vector Power Meter in Laboratory and Field Cycling.

To assess the validity and reliability of the Garmin Vector against the SRM power meter, 6 cyclists completed 3 continuous trials at power outputs from 100-300 W at 50-90 rev·min-1 and a 5-min time trial in laboratory and field conditions. In field conditions only, a 30-s sprint was performed. Data were compared with paired samples t-tests, with the 95% limits of agreement (LoA) and the typical error. Reliability was calculated as the coefficient of variation (CV). There was no significant difference between the devices in power output in laboratory (p=0.245) and field conditions (p=0.312). 1-s peak power was significantly different between the devices (p=0.043). The LoA were ~1.0±5.0 W and ~0.5±0.5 rev·min-1 in both conditions. The LoA during the 30-s sprint was 6.3±38.9 W and for 1-s peak power it was 18.8±17.1 W. The typical error for power output was 2.9%, while during sprint cycling it was 7.4% for 30-s and 2.7% for 1-s peak power. For cadence, the typical error was below 1.0%. The mean CVs were ~1.0% and ~3.0% for the SRM and Garmin, respectively. These findings suggest, that the Garmin Vector is a valid alternative for training. However, during sprint cycling there is lower agreement with the SRM power meter. Both devices provide good reliability (CV<3.0%).

Knowing your slope on the track: Getting the most out of GPS and power data

Introduction When cycling on hilly tracks, gravitational forces have a major influence on the cycling performance. For uphill tracks overcoming altitude differences can consume over 90% of the rider’s total power output [1]. Therefore, a good estimate of the slope is essential for simulating, predicting and optimizing the rider’s power output on a hilly course. In this work different methods to estimate slope profiles from recorded altitude, speed and power data were applied. The resulting slope profiles are compared with reference values gathered by differential GPS to quantify the error on the corresponding modelled power output.  Methods Three rides were performed on a 5.3 km long course in good weather conditions. Altitude, speed and power data were recorded with a Garmin Edge 1000 and an SRM power meter. To account for drifts due to the barometric sensor, the altitude was corrected with radar based elevation map data provided by http://www.gpsies.com (SRTM1 dataset). Additionally, highly precise differential GPS data recorded by a Leica GPS900 was available on most parts of the course (accuracy < 5 cm). The first three methods rely only on the altitude measurements: smoothing with a Gaussian filter and applying the differential quotient (GAU), smoothing with a Savitzky-Golay filter (SGO) and using the Tikhonov regularization method [2] (TIK). The forth method incorporated only smoothed speed and power measurements, by calculating the slope with the P-v model [3] (MOD). Additionally, the Tikhonov regularization approach was extended to include altitude as well as speed and power measurements (TIM). For each method and ride, the best parameter set was determined individually.  Results and Discussion Since we want to quantify the impact of errors in the slope estimation on the rider’s power output, instead of presenting the error of the slope directly, we prefer the corresponding error in power needed to overcome altitude levels: The change of potential energy P pot can be described as P pot =mgsv, where m is the mass of rider and bike, g is the gravitational acceleration, s the slope of the course and v the speed [3]. The speed was comparable on all three rides with mean values of 18.6 ± 0.4 km/h. Figure 1 gives an overview of the root mean square errors (RMSE) and the mean errors (ME) of the different slope estimation methods compared to the reference slope, which was determined by slightly smoothing the differential GPS altitude and calculating the difference quotient. For the altitude based methods GAU, SGO and TIK the altitude correction eliminates the ME of around 5 W while the RMSE is not changed. This confirms the usefulness of the altitude correction to globally removing the drift while locally preserving the quality of the barometric data. The model based method MOD reduces the RMSE from over 40 W (GAU and SGO) to less than 30 W. It should be noted that this method is highly sensitive to external factors like wind or braking. TIM avoids those problems by additionally considering altitude measurements. This leads to a comparable RMSE as MOD but less variance between the three recordings. It can be observed, that the ME is not zero if we take model estimations into account. This indicates, that small errors in the model introduce a drift over time. Therefore, for future applications, a correction algorithm should be developed.  Conclusions By representing the error in terms of the power output, a direct measure of the error that is introduced to the P-v model for simulation and prediction purposes is provided. With the altitude correction, the mean error diminishes, which avoids that the impact of modelling errors adds up over time. Moreover, the local modelling error can be reduced, by considering speed and power data in addition to the recorded error

A Comparison of the Accuracy and Reliability of the Wahoo KICKR and SRM Power Meter

The Wahoo KICKR cycling trainer is a new direct-drive electromagnetically braked bike-trainer that allows cyclists to use their own bicycles as ergometer. It is purported to provide ±3% accuracy in power, despite costing considerably less than other cycling ergometers. The purpose of this study was to assess the accuracy and reliability of several KICKR units against the more established SRM power meter using a first-principles based dynamic calibration rig (CALRIG).Five KICKRs and one SRM unit were assessed by a CALRIG-driven incremental test. Following a 15 min warm-up and ‘calibration’ as per manufacturer instructions, power was increased (starting at 50 W) by 50 W every 2 min up to 400 W. Each unit was tested twice non-consecutively, in random order. Data was recorded at 1 Hz, with the last 10 s of each stage being averaged for analysis. The mean error (%) and coefficient of determination (R 2 ) versus CALRIG; as well as the change in mean error and Typical Error of Measurement (TEM) (expressed as a % coefficient of variation) between trials was calculated for each device.The mean error across all KICKR units was -1.5% (range: -3.1% to 0.0%) compared to -1.6% reported by the SRM. R 2 >0.999 was found for all KICKR units and SRM compared to the CALRIG. The mean TEM for the KICKRs was 1.5% (range: 1.1% to 1.9%), whereas the SRM reported 0.7%. For test-retest reproducibility, two KICKRs had statistically significant changes in mean error, with an average 1.3% change across all KICKRs. Comparatively, the SRM reported a 0.4% change between trials. The Wahoo KICKR trainer measures power to a similar level of accuracy to the more reputable SRM power meter during an incremental exercise test. Although not as reproducible, the KICKR still demonstrates an acceptable level of reliability for assessing cycling performance.

Effect of gear ratio on peak power and time to peak power in BMX cyclists

The aim of this study was to ascertain if gear ratio selection would have an effect on peak power and time to peak power production in elite Bicycle Motocross (BMX) cyclists. Eight male elite BMX riders volunteered for the study. Each rider performed three, 10-s maximal sprints on an Olympic standard indoor BMX track. The riders’ bicycles were fitted with a portable SRM power meter. Each rider performed the three sprints using gear ratios of 41/16, 43/16 and 45/16 tooth. The results from the 41/16 and 45/16 gear ratios were compared to the current standard 43/16 gear ratio. Statistically, significant differences were found between the gear ratios for peak power (F(2,14) = 6.448; p = .010) and peak torque (F(2,14) = 4.777; p = .026), but no significant difference was found for time to peak power (F(2,14) = 0.200; p = .821). When comparing gear ratios, the results showed a 45/16 gear ratio elicited the highest peak power,1658 ± 221 W, compared to 1436 ± 129 W and 1380 ± 56 W, for the 43/16 and 41/16 ratios, respectively. The time to peak power showed a 41/16 tooth gear ratio attained peak power in −0.01 s and a 45/16 in 0.22 s compared to the 43/16. The findings of this study suggest that gear ratio choice has a significant effect on peak power production, though time to peak power output is not significantly affected. Therefore, selecting a higher gear ratio results in riders attaining higher power outputs without reducing their start time.

Agreement between LeMond Revolution cycle ergometer and SRM power meter during power profile and ramp protocol assessments

This study aimed to evaluate the agreement in cycling power output measurements between the LeMond Revolution cycle ergometer and SRM power meter. The LeMond Revolution measures power output via removal of the rear bicycle wheel and attaching it using a quick-release system, estimating power output through a head-unit that processes drive-train resistance and atmospheric conditions. Fourteen well-trained cyclists completed incremental protocols and power profile assessments on a bicycle fitted with SRM scientific power meter and attached to a LeMond Revolution cycle ergometer. Power output was measured by both devices at 1 Hz. Data from each device were compared using Pearson’s correlations, paired t-tests, assessments of heteroscedasticity, Bland-Altman plots and 95% limits of agreement. During incremental tests, errors in power measurement of the LeMond Revolution progressively increased at greater power outputs when compared with SRM (bias: 2-34 W; CV 1.5-6.7%). During power profile assessments, errors in mean power measurement of the LeMond Revolution were also slightly overestimated for all efforts from a rolling start (+3 ± 8%; CV = 5.1%). Conversely, the LeMond Revolution underestimated peak power output during five second sprint efforts and the greatest error was observed between measurements for mean power output during a five second sprint from a stationary start (-7 ± 24%; CV = 10.6%). Overall, the LeMond Revolution is a practical, cost-effective alternative to more expensive ergometers for detecting large changes in mean power output. However, high level of error during high-intensity sprint efforts from a stationary start is a limitation for well-trained sprint cyclists.

Agreement of Power Measures between Garmin Vector and SRM Cycle Power Meters

ABSTRACTThis study aimed to determine if the Garmin Vector (Schaffhausen, Switzerland) power meter produced acceptable measures when compared with the Schoberer Rad Messetechnik (SRM; Julich, Germany) power meter across a range of high-intensity efforts. Twenty-one well-trained cyclists completed power profiles (seven maximal mean efforts between 5 and 600 s) using Vector and SRM power meters. Data were compared using assessments of heteroscedasticity, t tests, linear regression, and typical error of estimate (TEE). The data were heteroscedastic, whereby the Vector pedals increasingly overestimated values at higher power outputs; however, t tests did not identify any significant differences between power meters (p > .05). Using linear regression, Vector data were fit to an SRM equivalent (slope = .99; intercept = −9.87) and TEE produced by this equation was 3.3% (3.0%–3.8%). While the data shows slight heteroscedasticity due to differing strain-gauge placement and resultant torque measurement variance, the Vector appears acceptable for measures of power output across various cycling efforts.

Physical Demands of Sprinting in Professional Road Cycling.

The aim of this study was to quantify the demands of road competitions ending with sprints in male professional cycling. 17 races finished with top-5 results from 6 male road professional cyclists (age, 27.0±3.8 years; height, 1.76±0.03 m; weight, 71.7±1.1 kg) were analysed. SRM power meters were used to monitor power output, cadence and speed. Data were averaged over the entire race, different durations prior to the sprint (60, 10, 5 and 1 min) and during the actual sprint. Variations in power during the final 10 min of the race were quantified using exposure variation analysis. This observational study was conducted in the field to maximize the ecological validity of the results. Power, cadence and speed were statistically different between various phases of the race (p<0.001), increasing from 316±43 W, 95±4 rpm and 50.5±3.3 km·h(-1) in the last 10 min, to 487±58 W, 102±6 rpm and 55.4±4.7 km·h(-1) in the last min prior to the sprint. Peak power during the sprint was 17.4±1.7 W·kg(-1). Exposure variation analysis revealed a significantly greater number of short-duration high-intensity efforts in the final 5 min of the race, compared with the penultimate 5 min (p=0.010). These findings quantify the power output requirements associated with high-level sprinting in men's professional road cycling and highlight the need for both aerobic and anaerobic fitness.

Agreement Between the Stages Cycling and SRM Powermeter Systems during Field-Based Off-Road Climbing.

The aim of this study was to determine the agreement between two portable cycling powermeters for use doing field based mountain biking. A single participant performed 15 timed ascents of an off-road climbs. The participants bicycle was instrumented with Stages Cycling and SRM powermeters. Mean and peak power output and cadence were recorded at 1 s intervals by both systems. Significant differences were determined using paired t-tests, whilst agreement was determined using 95% ratio limits of agreement (LoA). Significant differences were found between the two systems for mean power output (p<.001), with the Stages powermeter under reporting power by 8 % compared to the SRM. LoA for mean power output were 0.92 ×÷ 1.02 (95% LoA = 0.90 – 0.93). Peak power output was also significantly lower with the Stages powermeter (p=.02) by 5 % when compared to the SRM powermeter. LoA for peak power output were 0.94 ×÷ 1.09 (95% limits of agreement = 0.87 – 1.03). Significant differences were found for mean cadence between the two powermeters (p=.009), with LoA being 0.99 ×÷ 1.01 (95% limits of agreement = 0.99 – 1.00). This study found that though the Stages Cycling powermeter provided a reliable means of recording power output and cadence, the system significantly underestimated mean and peak power output when compared with the SRM system. This may in part be due to differences in strain gauge configuration and the subsequent algorithms used in the calculation of power output and the potential influence of bilateral imbalances within the muscles may have on these calculations.

Static and dynamic evaluation of a pedal system for measuring three-dimensional forces in cycling

The purpose of this study was to determine the accuracy of a newly developed pedal system (JA:Ped3) for measuring three-dimensional pedal forces in laboratory conditions in cycling. Three-dimensional force measurements were obtained using 12 strain gauges in each pedal, and the pedal angle was measured with a rotary potentiometer mounted on the pedal axle. The pedal forces were validated in a static condition by comparing a range of known forces and in dynamic conditions using the JA:Ped3 with the data collected simultaneously by two commercially available systems, SRM Powermeter and PowerForce. An incremental cycling test was performed with seven elite cyclists. In the static measurements, JA:Ped3 showed an average deviation of &lt;2% and the maximum absolute error did not exceed 4.2 N. In the dynamic measurements, JA:Ped3-calculated pedal torque and power output yielded similar values to the corresponding data from the SRM Powermeter and showed an average deviation of &lt;3%. Using the JA:Ped3 in a laboratory setting for testing, elite cyclists revealed plausible results. The pedal is lightweight and data transmission could be implemented to offer a device for laboratory and field measurements. This pedal would be specifically helpful for cyclists, coaches, therapists and scientists.

The physical, mental and hormonal responses to short-term intensified training in well-trained cyclists with a high carbohydrate nutritional intervention

Background: Short periods of intensified training (IT) are a regular feature in a cyclist’s training programme. The aim of this study was to monitor changes in performance, mood state and plasma hormones during short term IT in well-trained cyclists and the effects of a carbohydrate nutritional intervention. Methods: Thirteen highly trained male cyclists (age: 25±6 y, V I‡O2max: 72±5 ml/kg/min) participated in two 9-day periods of intensified training while ingesting either high (HCHO) or low (LCHO) carbohydrate (CHO) beverages before (24 g vs 2 g CHO), during (60 g/h vs 20 g/h CHO) and after (44 g vs 10 g CHO) every training session. All training sessions were monitored with SRM power meters and heart rate (HR) monitors. Performance was assessed throughout IT with maximal oxygen uptake (V I‡O2max) and 1 h time trial (TT). Mood state questionnaires (POMS and DALDA) were completed daily to monitor total mood disturbance (TMD). Venous blood samples were collected before and immediately after each TT and analysed for brain-derived neurotrophic factor (BDNF), cortisol (CORT), testosterone (TEST), CORT/TEST ratio, adrenocorticotrophic hormone (ACTH) and prolactin (PRL). Results: Weekly training volume and intensity (time spent in the three highest HR zones) increased significantly during IT (9.3±2.4 h to 23.5±3.4 h/week for volume and 2.6±2.5 to 6.5±4.0 h/week for intensity). Total energy intake and dietary CHO were significantly higher in HCHO than LCHO (4148±766 vs 3501±616 kcal/d and 679±105 vs 505±107 g/d CHO). TMD was elevated during IT (p<0.05). Overall, POMS TMD was significantly higher in LCHO than HCHO (p=0.04). Performance in the a€–V I‡Oa€—_2max exercise protocol fell significantly with IT in both conditions (p<0.05): peak power (391±37 to 375±38 W), maximum HR (190±10 to 179±8 bpm) and completion time (31:15±03:12 to 30:00±03:30 min:s). No significant changes in TT performance were observed over IT, or between conditions. However, absolute mean power during TT decreased non-significantly in both conditions (-1.2 W (-1%) and -7.2 W (-3%) for HCHO and LCHO, respectively). Mean HR tended to decrease from TT1-TT3 in LCHO (p=0.06). Pre-exercise CORT and BDNF tended to be lower after IT in both conditions (p=0.07) and pre-exercise ACTH tended to be lower in LCHO than HCHO (p=0.06). Post-exercise CORT, ACTH, BDNF, CORT/TEST ratio and PRL concentrations decreased with IT (p<0.05), with no difference between conditions. The post-exercise increase in PRL seen at TT1 did not occur at TT3; instead PRL concentrations fell from 373 to 348 mIU/L. Post-exercise TEST was lower in LCHO than HCHO (13.2±3.6 vs 14.6±3.8 nmol/L; p=0.03).  Discussion: A relatively short period of IT resulted in significant disturbances in mood state and minor performance decrements. LCHO resulted in significantly greater TMD than HCHO. Previous studies have reported significant declines in performance with similar exercise protocols; yet this group of well-trained athletes showed a level of resistance to the substantial increase in training load. However, hormonal changes present signs of chronic stress, including decreased post-exercise PRO, BDNF, CORT, ACTH and CORT/TEST suggesting these cyclists may have exhibited early signs of overreaching.

Validity and reliability of the look Keo power pedal system for measuring power output during incremental and repeated sprint cycling.

Power meters have traditionally been integrated into the crank set, but several manufacturers have designed new systems located elsewhere on the bike, such as inside the pedals. This study aimed to determine the validity and reliability of the Keo power pedals during several laboratory cycling tasks. Ten active male participants (mean ± SD age 34.0 ± 10.6 y, height 1.77 ± 0.04 m, body mass 76.5 ± 10.7 kg) familiar with laboratory cycling protocols completed this study. Each participant was required to complete 2 laboratory cycling trials on an SRM ergometer (SRM, Germany) that was also fitted with the Keo power pedals (Look, France). The trials consisted of an incremental test to exhaustion followed by 10 min rest and then three 10-s sprint tests separated by 3 min of cycling at 100 W. Over power ranges of 75 to 1147 W, the Keo power-pedal system produced typical error values of 0.40, 0.21, and 0.21 for the incremental, sprint, and combined trials, respectively, compared with the SRM. Mean differences of 21.0 and 18.6 W were observed between trials 1 and 2 with the Keo system in the incremental and combined protocols, respectively. In contrast, the SRM produced differences of 1.3 and 0.6 W for the same protocols. The power data from the Keo power pedals should be treated with some caution given the presence of mean differences between them and the SRM. Furthermore, this is exacerbated by poorer reliability than that of the SRM power meter.

Analysis of cadence/heart rate (RPM/HR) versus power output (PO) during incremental test in cyclists

Studies on cycling cadence have been mainly focussed on the optimisation of cycling efficiency [1-3]. In order to obtain new insight into physiological indicators of performance, the present study aimed to evaluate the relationship between freely chosen cadence (FCC), heart rate (HR) and power output (PO) profiles in members of professional and non professional cycling teams during an incremental test (10W 30 sec). Heterogeneous group of 25 male cyclists performed a maximal incremental test on SRM powermeter (SRM Training Systems, Julich, Germany) and FCC/HR vs PO was measured. In all subjects the FCC/HR vs PO obtained showed a linear phase followed by a curvilinear phase starting at comparable FCC/HR value in all subjects (0,62 ± 0,06 SD; ES: 0,01, IC95%: 0,60 - 0,65, IC99%: 0.59 - 0.65). Whether the observed deflection point corresponds to anaerobic threshold deserves future investigation.

Aerodynamic drag and biomechanical power of a track cyclist as a function of shoulder and torso angles

The speed attained by a track cyclist is strongly influenced by aerodynamic drag, being the major retarding force in track events of more than 200 m. The aims of this study were to determine the effect of changes in shoulder and torso angles on the aerodynamic drag and power output of a track cyclist. The drag of three competitive track cyclists was measured in a wind tunnel at 40 kph. Changes in shoulder and torso angles were made using a custom adjustable handlebar setup. The power output was measured for each position using an SRM Power Meter. The power required by each athlete to maintain a specific speed in each position was calculated, which enabled the surplus power in each position to be determined. The results showed that torso angle influenced the drag area and shoulder angle influenced the power output, and that a low torso angle and middle shoulder angle optimised the surplus power. However, the lowest possible torso angle was not always the best position. Although differences between individual riders was seen, there was a strong correlation between torso angle and drag area.

No Difference In Sprint Cycling Performance Tests On A Stationary And Mobile Ergometer

PURPOSE: To determine if there is a difference in sprint cycling performance performed on a stationary ergometer indoors compared to sprint cycling performance a regular road racing bicycle outdoors. METHODS: Eight national level cyclists, (Age 21.3 ± 3.3 years, Weight 74.4 ± 10 kg, Height 178 ± 4.7 cm) volunteered to participate in the study. Subjects performed two different maximal sprint cycling tests in a randomized crossover design on a traditional weight basket ergometer equipped with a SRM Professional powermeter, and on a regular road racing bicycle equipped with the same SRM powermeter. The two tests were a high speed sprint starting a ∼100 RPM and a low speed sprint from a standing start. Each subject performed 3 sprints on each ergometer and condition, and the highest 0.5 s value was reported. All values are reported as mean ± standard deviation. Students T-test was used to test for a difference between ergometers. RESULTS: Maximal power output in the high speed sprint was 1325 ± 317 W for the stationary ergometer and 1300 ± 305 W for the mobile ergometer. Maximal power output in the low speed sprint was 1414 ± 378 W for the stationary ergometer and 1281 ± 170 W, with no significant difference between the two different ergometers. CONCLUSIONS: There was no significant difference between maximal sprint cycling tests performed indoors on a stationary weight basket ergometer and on a regular road racing bicycle performed outdoors.

Critical Power And Aerodynamic Drag Accurately Predict Road Time-trial Performance In British Champion Cyclists

PURPOSE: This case study examined the efficacy of wind-tunnel derived measures of aerodynamic drag combined with the critical power model to predict road cycling time trial performance. METHODS: One male (73.1 kg) and one female (68.0 kg) cyclist each won individual time trial races against the clock over 10, 25, 50 and 100 miles (16.1, 40.2, 80.5, 160.9 km; British Championships; out-and-back road courses). Power was recorded with a SRM Powermeter (SRM, Germany). Separately, critical power (CP) and anaerobic work capacity (AWC) were determined from the power-time relationship of race efforts of 20 to 50 min. Aerodynamic drag force was determined in a wind-tunnel at 13.9 m/s at 0° yaw (Mercedes GP F1). Course elevation profile was obtained via GPS records and cycling speed was predicted both a priori and in retrospect from estimated or known power-time data using the equations of Martin et al., (2006). RESULTS: CP was 416 and 299 W; AWC was 25.6 and 15.6 kJ (male and female respectively). Over the shortest duration race, predicted and actual race powers closely agreed, but not in longer races. When the actual race power was used in retrospect to predict speed, the difference between the actual and predicted times was on average 0.2%, and no greater than 1.1%, with no apparent bias. CONCLUSIONS: The predictive strength of the power-speed relationship (previously established for track cycling) holds true on the open road and allows a remarkably close estimate of race time. Small differences between the predicted and actual times are most likely derived from the effect of the prevailing wind and unaccounted-for differences in rolling resistance as well as the variable distribution of power across the course. These latter points will be elaborated upon.Table