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Abstract

Purpose: To validate the new drive indoor trainer Hammer designed by Cycleops®. Methods: Eleven cyclists performed 44 randomized and counterbalanced graded exercise tests (100-500W), at 70, 85 and 100 rev.min-1 cadences, in seated and standing positions, on 3 different Hammer units, while a scientific SRM system continuously recorded cadence and power output data. Results: No significant differences were detected between the three Hammer devices and the SRM for any workload, cadence, or pedalling condition (P value between 1.00 and 0.350), except for some minor differences (P 0.03 and 0.04) found in the Hammer 1 at low workloads, and for Hammer 2 and 3 at high workloads, all in seated position. Strong ICCs were found between the power output values recorded by the Hammers and the SRM (≥0.996; P=0.001), independently from the cadence condition and seated position. Bland-Altman analysis revealed low Bias (-5.5-3.8) and low SD of Bias (2.5-5.3) for all testing conditions, except marginal values found for the Hammer 1 at high cadences and seated position (9.6±6.6). High absolute reliability values were detected for the 3 Hammers (150-500W; CV<1.2%; SEM<2.1). Conclusions: This new Cycleops trainer is a valid and reliable device to drive and measure power output in cyclists, providing an alternative to larger and more expensive laboratory ergometers, and allowing cyclists to use their own bicycle.

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... Portable power meter devices overcome important drawbacks of laboratory testing, allowing the use of cyclists' own bicycles, so that decisive metrics such as the crank width (Q-factor), crank length, and geometry-related variables are replicated in the test [3]. Commercial indoor stationary cycle training, cycling treadmills, or rollers are a valid and reliable alternative to recreate outdoor cycling conditions, both for testing [4][5][6] and training [7]. While these tools simulate outdoor cycling, they do not allow recording during real outdoor environments (e.g., missing air drag and downhill sections or increasing dehydration), which may alter the metrics [8,9] and limit to apply the results to real-life situations. ...
... For the seated and standing GXTs, as well as the vibration tests, the SRM 172.5 mm crank power meter was fixed on a medium-size road bicycle (2010 Giant Giant-Bicycles, Taiwan; Aluminum alloy frame with carbon fiber fork). The rear wheel of the bicycle was removed and attached to a calibrated Cycleops Hammer [6] device with 10 speed (11-25 tooth) rear gear ratio and 39 to 53 tooth front gear ratio. For all tests, the gear ratio 53 × 15 was selected, and cyclists were not allowed to change it to prevent a potential effect of this variable on pedaling technique. ...
... All tests began with a standardized warm-up of 5 min at 75 W with a free chosen cadence and the Hammer set in the hyperbolic mode. Thereafter, subjects performed three randomized and counterbalanced 1-min GXT in seated position, one for each selected fixed cadence (70, 85, and 100 rpm), at six sub-maximal workloads (i.e., 100, 150, 200, 250, 300, and 350 W), separated by 4 min of recovery at 75 W with free chosen cadence [6] (Figure 1). The order of the three cadence levels was randomized to ensure that results were not altered due to increments on the ergometer break temperature or by the cyclists' fatigue. ...
Article
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This study aimed to examine the validity and reliability of the recently developed Assioma Favero pedals under laboratory cycling conditions. In total, 12 well-trained male cyclists and triath- letes (VO2max = 65.7 ± 8.7 mL·kg−1·min−1) completed five cycling tests including graded exercises tests (GXT) at different cadences (70–100 revolutions per minute, rpm), workloads (100–650 Watts, W), pedaling positions (seated and standing), vibration stress (20–40 Hz), and an 8-s maximal sprint. Tests were completed using a calibrated direct drive indoor trainer for the standing, seated, and vibration GXTs, and a friction belt cycle ergometer for the high-workload step protocol. Power output (PO) and cadence were collected from three different brand, new pedal units against the gold-standard SRM crankset. The three units of the Assioma Favero exhibited very high within-test reliability and an extremely high agreement between 100 and 250 W, compared to the gold standard (Standard Error of Measurement, SEM from 2.3–6.4 W). Greater PO produced a significant underestimating trend (p < 0.05, Effect size, ES ≥ 0.22), with pedals showing systematically lower PO than SRM (1–3%) but producing low bias for all GXT tests and conditions (1.5–7.4 W). Furthermore, vibrations ≥ 30 Hz significantly increased the differences up to 4% (p < 0.05, ES ≥ 0.24), whereas peak and mean PO differed importantly between devices during the sprints (p < 0.03, ES ≥ 0.39). These results demon- strate that the Assioma Favero power meter pedals provide trustworthy PO readings from 100 to 650 W, in either seated or standing positions, with vibrations between 20 and 40 Hz at cadences of 70, 85, and 100 rpm, or even at a free chosen cadence.
... The rear wheel of the bicycle was removed and attached to a calibrated CycleOps Hammer ( Q8 CycleOps, Madison, WI). 8 For the vibration tests, the whole system (CycleOps Hammer and bicycle) was installed over a vibrating plate ( Q9 Merit Fitness V2000), which provided vibrations in the vertical plane, simulating common situations caused by cobblestone roads or other rough terrains. 10 The front fork of the bicycle was attached to a Kickr Climb Indoor Grade Simulator (Wahoo Fitness, Atlanta, GA) to compensate for the height of the vibration platform (19.5 cm), thus achieving a 0% slope. ...
... Pallarés (jgpallares@um.es) is corresponding author. (70, 85, and 100 revolutions per minute [rpm]), at 6 submaximal workloads (100, 150, 200, 250, 300, and 350 W), separated by 4 minutes of recovery at 75 W with a free chosen cadence.8 ...
Article
Purpose: To examine the reproducibility (intradevice and interdevice agreement) of the Rotor 2INpower device under a wide range of cycling conditions. Methods: Twelve highly trained male cyclists and triathletes completed 5 cycling tests, including graded exercise tests at different cadences (70-100 rpm), workloads (100-650 W), pedaling positions (seated and standing), and vibration conditions (20-40 Hz) and an 8-second maximal sprint (>1000 W). An intradevice analysis included a comparison between the power output registered by 3 units of Rotor 2INpower, whereas the power output provided by each one of these units and the gold-standard SRM crankset were compared for the interdevice analysis. Among others, statistical calculations included the standard error of measurement, expressed in absolute (in watts) and relative terms as the coefficient of variation (CV). Results: Except for the graded exercise test seated at 100 rpm/100 W (CV = 10.2%), the intradevice analysis showed an acceptable magnitude of error (CV ≤ 6.9%, standard error of measurement ≤ 12.3 W) between the 3 Rotor 2INpower. Similarly, these 3 units showed an acceptable agreement with the gold standard in all graded exercise test situations (CV ≤ 4.0%, standard error of measurement ≤ 13.1 W). On the other hand, both the intradevice and interdevice agreements proved to be slightly reduced under high cadences (intradevice: CV ≤ 10.2%; interdevice: CV ≤ 4.0%) and vibration (intradevice: CV ≤ 4.0%; interdevice: CV ≤ 3.6%), as well as during standing pedaling (intradevice: CV ≤ 4.1%; interdevice: CV ≤ 2.5%). Although within the limits of an acceptable agreement, measurement errors increased during the sprint tests (CV ≤ 7.4%). Conclusions: Based on these results, the Rotor 2INpower could be considered a reproducible tool to monitor power output in most cycling situations.
... Las diferencias entre los resultados obtenidos por todos estos trabajos precedentes y el nuestro pueden estar relacionadas con importantes diferencias metodológicas y el control al que sometieron los investigadores a distintas variables que pueden considerarse contaminantes del resultado. En primer lugar, a los participantes de nuestro estudio, gracias a la mejora tecnológica de los nuevos rodillos de freno electromagnético (Lillo-Bevia & Pallarés, 2017), se les permitió pedalear a sus cadencias libremente escogidas, mientras la carga se mantuvo estable. Tratando de controlar la influencia en la carga que modificaciones sustanciales de la cadencia pudiesen producir en los cicloergómetros utilizados por alguno de los estudios precedentes de TLIM a MLSS (de Oliveira Cruz et al., 2015;Fontana et al., 2009;Mendes et al., 2013), los ciclistas participantes se vieron obligados a mantener una cadencia fija durante todo el protocolo que les permitiese a su vez, mantener la carga objetivo. ...
... Estas bicicletas fueron instaladas en el rodillo con freno electromagnético Cycleops Hammer (CycleOps, Madisson, EE. UU.)(Lillo-Bevia & Pallarés, 2017). Se solicitó a los participantes que pedalearan sentados en todo momento con el fin de controlar las posibles diferencias en la economía del pedaleo(Arkesteijn et al., 2016).Igualmente, se les permitió elegir libremente su cadencia preferida(Denadai et al., 2006). ...
Thesis
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The doctoral thesis presented in this document is structured in three different parts. The first part of the work is composed of studies I and II, where the validation work of two different workload cycling tools, “drive indoor trainer Cycleops Hammer” and “PowerTap P1 Pedals Power Meter “, is detailed. In both articles, randomized and counterbalanced incremental workload tests (100-500 W) were performed, at 70, 85 and 100 rev·min-1 cadence, with sitting and standing pedalling in 3 different Hammer unit cadences. Then, the results are compared against the values measured by a professional SRM crankset. In general terms, no significant differences were detected between the Hammer devices and the SRM, while strong intraclass correlation coefficients were observed (≥0.996; p=0.001), with low bias (-5,5 a 3,8), and high values of absolute reproducibility (CV<1,2%, SEM<2,1). The PowerTap P1 pedals showed strong correlation coefficients in a seated position (rho ≥ 0.987). They underestimated the power output obtained in a directly proportional way to the cadence, with an average error of 1.2%, 2.7%, 3.5% for 70, 85 and 100 rev∙min-1. However, they showed high absolute reproducibility values (150-500 W, CV = 2.3%, SEM <1.0W). These results prove that both are valid and reproducible devices to measure the power output in cycling, although caution should be exercised in the interpretation of the results due to the slight underestimation. The second part of the thesis is devoted to the study III, where the time to exhaustion (TTE) at the workloads related to the main events of the aerobic and anaerobic pathway in cycling were analysed in duplicate in a randomized and counterbalanced manner (Lactic anaerobic capacity (WAnTmean), the workload that elicit VO2max -MAP-, Second Ventilatory Threshold (VT2) and at Maximal Lactate Steady State (MLSS). TTE values were 00:28±00:07, 03:27±00:40, 11:03±04:45 and 76:35±12:27 mm:ss, respectively. Moderate between-subject reproducibility values were found (CV=22.2%,19.3%;43.1% and 16.3%), although low within-subject variability was found (CV=7.6%,6.9%;7.0% y 5.4%). According to these results, the %MAP where the physiological events were found seems to be a useful covariable to predict each TTE for training or competing purposes. Finally, in the third part of the work, the results of studies IV y V have been included. The validity of two different methods to estimate the cyclists’ workload at MLSS was evaluated. The first method was a 20 min time trial test (20TT), while the second method was a one-day incremental protocol including 4 steps of 10 minutes (1day_MLSS). The 20TT test absolute reproducibility, performed in duplicate, was very high (CV = -0.3±2.2%, ICC = 0.966, bias = 0.7±6.3 W). 95% of the mean 20TT workload overestimated the MLSS (bias 12.3±6.1W). In contrast, 91% of 20TT showed an accurate prediction of MLSS (bias 1.2±6.1 W), although the regression equation "MLSS (W) = 0.7489 * 20TT (W) + 43.203" showed even a better MLSS estimates (bias 0.1±5.0 W). Related to the 1day_MLSS test, the physiological steady state was determined as the highest workload that could be maintained with a [Lact] rise lower than 1mmol·L-1. No significant differences were detected between the MLSS (247±22 W) and the main construct of the test (DIF_10to10) (245±23 W), where the difference of [Lact] between minute 10 of two consecutive steps were considered, with high correlations (ICC = 0.960), low bias (2.2W), as well as high within-subject reliability (ICC = 0.846, CV = 0.4%, Bias = 2.2±6.4W). Both methods were revealed as valid predictors of the MLSS, significantly reducing the requirements needed to individually determine this specific intensity.
... In addition, cyclists used their own cycling shoes fitted with Look cleats. The validity of the FAD was assessed in the laboratory using protocols derived from those used in previous studies [14,22]. We compared PO and cadence using different PO values, pedaling cadences and cycling positions (see Figure 2 for a graphical summary of the protocol). ...
... Moreover, we found a quasi-perfect correlation between the SRM and FAD devices for this outcome in the sprints block. The CV of the PO meters for block one (submaximal incremental test) shows similarities to previous studies comparing pedal power meters and the SRM device [1,22] and are consistent with the CV of the SRM obtained by Hutchinson et al. [35], whereas the CV of the Vector Pedals was higher. Likewise, in a study by Duc et al. [17], the CV of the ErgomoPro Power meter was higher than the CV of SRM and Powertap Hub. ...
Article
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Cycling power meters enable monitoring external loads and performance changes. We aimed to determine the concurrent validity of the novel Favero Assioma Duo (FAD) pedal power meter compared with the crank-based SRM system (considered as gold standard). Thirty-three well-trained male cyclists were assessed at different power output (PO) levels (100-500 W and all-out 15-s sprints), pedaling cadences (75-100 rpm) and cycling positions (seating and standing) to compare the FAD device vs SRM. No significant differences were found between devices for cadence nor for PO during all-out efforts (p > 0.05), although significant but small differences were found for efforts at lower PO values (p < 0.05 for 100-500 W, mean bias 3-8 W). A strong agreement was observed between both devices for mean cadence (ICC > 0.87) and PO values (ICC > 0.81) recorded in essentially all conditions and for peak cadence (ICC > 0.98) and peak PO (ICC > 0.99) during all-out efforts. The coefficient of variation for PO values was consistently lower than 3%. In conclusion, the FAD pedal-based power meter can be considered an overall valid system to record PO and cadence during cycling, although it might present a small bias compared with power meters placed on other locations such as SRM.
... After a standardized warm-up (10 min at 75 watts), the participants performed a maximal incremental cycling test on their own bicycle, attached to an indoor trainer (Hammer; CycleOps, Madison, WI). 17 The test started at 75 watts, and the PO was increased by 5 watts every 12 seconds (averaging 60 watts/min) until the participants reached volitional exhaustion or could no longer maintain cadence over 70 revolutions per minute. Breath-by-breath gas exchange data were collected during the test (Ultima Series Medgraphics; Cardiorespiratory Diagnostics, Saint Paul, MN). ...
Purpose: To compare the effectiveness of resistance power training (RPT, training with the individualized load and repetitions that maximize power output) and cycling power training (CPT, short sprint training) in professional cyclists. Methods: The participants (20 [2] y, peak oxygen uptake 78.0 [4.4] mL·kg −1 ·min −1) were randomly assigned to perform CPT (n = 8) or RPT (n = 10) in addition to their usual training regime for 7 weeks (2 sessions/wk). The training loads were continuously registered using the session rating of perceived exertion. The outcomes included endurance performance (8-min time trial and incremental test), as well as measures of muscle strength/power (1-repetition maximum and mean maximum propulsive power on the squat, hip thrust, and lunge exercises) and body composition (assessed by dual-energy X-ray absorptiometry). Results: No between-group differences were found for training loads or for any outcome (P > .05). Both interventions resulted in increased time-trial performance, as well as in improvements in other endurance-related outcomes (ie, ventilatory threshold, respiratory compensation point; P < .05). A significant or quasi-significant increase (P = .068 and .047 for CPT and RPT, respectively) in bone mineral content was observed after both interventions. A significant reduction in fat mass (P = .017), along with a trend (P = .059) toward a reduced body mass, was observed after RPT, but not CPT (P = .076 for the group × time interaction effect). Significant benefits (P < .05) were also observed for most strength-related outcomes after RPT, but not CPT. Conclusion: CPT and RPT are both effective strategies for the improvement of endurance performance and bone health in professional cyclists, although the latter tends to result in greater improvements in body composition and muscle strength/power.
... This fact could introduce a source of error in the power measurement, since in some cases the accuracy of this variable (resistance × velocity) decreases as a consequence of an overestimation of the resistance parameter [19]. Advances in technology have led to the development of power meters that directly measure the instantaneous force applied to the pedals, plate, or rear-wheel axle [20,21]. Thus, instantaneous power output could be obtained by means of the simultaneous measure of the angular velocity. ...
Article
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This study aimed to analyze the validity and sensitivity of two time-shortened Wingate anaerobic tests (WAnTs), by means of three phases. In Phase A, 40 participants performed a traditional 30 s WAnT, whereas the first 15 s (WAnT15) and 20 s (WAnT20) were used to elaborate two predictive models. In Phase B, another 30 s WAnT was performed by 15 different volunteers to examine the error of these models (cross-validation). Finally, in Phase C, a 30 s WAnT was registered before and after a 10-week velocity-based training conducted by 22 different participants (training group, TRAIN = 11; control group that fully refrained from any type of training, CONTROL = 11). Power changes (in Watts, W) after this training intervention were used to interpret the sensitivity of the time-shortened WAnT. Adjusted coefficient of determination (R2) was reported for each regression model, whereas the cross-validation analysis included the smallest detectable change (SDC) and bias. Close relationships were found between the traditional 30 s WAnT and both the WAnT15 (R2 = 0.98) and WAnT20 (R2 = 0.99). Cross-validation analysis showed a lower error and bias for WAnT20 (SDC = 9.3 W, bias = −0.1 W) compared to WAnT15 (SDC = 22.2 W, bias = 1.8 W). Lastly, sensitivity to identify individual changes was higher for WAnT20 (TRAIN = 11/11 subjects, CONTROL = 9/11 subjects) than for WAnT15 (TRAIN = 4/11 subjects, CONTROL = 2/11 subjects). These findings suggest that the WAnT20 could become a valid and sensitive protocol to replace the traditional 30 s WAnT.
... To ensure comfort, all endurance-related outcomes were assessed with the participants' own bicycle attached to a validated indoor trainer (Hammer; CycleOps, Madison, WI). 24 After a standardized 10-minute warm-up at 75 W, participants performed a maximal incremental cycling test. The test started at 75 W and PO was increased following a ramp-like protocol (ie, 5-W increases every 12 s to an average 25 W/min), while breath-bybreath gas exchange data were collected (Ultima Series Medgraphics; Cardiorespiratory Diagnostics, Saint Paul, MN). ...
Article
Purpose: To compare the effectiveness of optimum power load training (OPT, training with an individualized load and repetitions that maximize power output) and traditional resistance training (TRT, same number of repetitions and relative load for all individuals) in professional cyclists. Methods: Participants (19 [1] y, peak oxygen uptake 75.5 [6] mL/kg/min) were randomly assigned to 8 weeks (2 sessions per week) of TRT (n = 11) or OPT (n = 9), during which they maintained their usual cycle training schedule. Training loads were continuously registered, and measures of muscle strength/power (1-repetition maximum and maximum mean propulsive power on the squat, hip thrust, and lunge exercises), body composition (assessed by dual-energy X-ray absorptiometry), and endurance performance (assessed on both an incremental test and an 8-min time trial) were collected before and at the end of the intervention. Results: OPT resulted in a lower average intensity (percentage of 1-repetition maximum) during resistance training sessions for all exercises (P < .01), but no differences were found for overall training loads during resistance or cycling sessions (P > .05). Both programs led to significant improvements in all strength/power-related parameters, muscle mass (with no changes in total body mass but a decreased fat mass), and time-trial performance (all Ps < .05). A trend toward increased power output at the respiratory compensation point was also found (P = .056 and .066 for TRT and OPT, respectively). No between-groups differences were noted for any outcome (P > .05). Conclusion: The addition of either TRT or OPT to an endurance training regimen of elite cyclists results in similar improvements of body composition, muscle strength/power, and endurance performance.
... Cy- clists used their own cycling shoes fitted with Look cleats. The absolute and relative validity of this direct drive device has been recently confirmed (Lillo-Bevia and Pallares, 2017). ...
Article
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To validate the new PowerTap P1® pedals power meter (PP1), thirty-three cyclists performed 12 randomized and counterbalanced graded exercise tests (100–500 W), at 70, 85 and 100 rev·min-1 cadences, in seated and standing positions. A scientific SRM system and a pair of PP1 pedals continuously recorded cadence and power output data. Significantly lower power output values were detected for the PP1 compared to the SRM for all workloads, cadences, and pedalling conditions (2–10 W, p < 0.05), except for the workloads ranged between 150 W to 350 W at 70 rev·min-1 in seated position (p > 0.05). Strong Spearman’s correlation coefficients were found between the power output values recorded by both power meters in a seated position, independently from the cadence condition (rho ≥ 0.987), although slightly lower concordance was found for the standing position (rho = 0.927). The mean error for power output values were 1.2%, 2.7%, 3.5% for 70, 85 and 100 rev·min-1, respectively. Bland-Altman analysis revealed that PP1 pedals underestimate the power output data obtained by the SRM device in a directly proportional manner to the cyclist’s cadence (from -2.4 W to -7.3 W, rho = 0.999). High absolute reliability values were detected in the PP1 pedals (150–500 W; CV = 2.3%; SEM < 1.0 W). This new portable power meter is a valid and reliable device to measure power output in cyclists and triathletes for the assessment, training and competition using their own bicycle, although caution should be exercised in the interpretation of the results due to the slight power output underestimation of the PP1 pedals when compared to the SRM system and its dependence on both pedalling cadence and cyclist’s position (standing vs. seated).
... The effects of position on PO measurements have been tested in several studies [36,51,69,77,89,94,106] to assess the sensitivity of power meters. Bertucci, Duc, Villerius, Pernin, and Grappe [36] reported that the PowerTap rear hub was not significantly affected by the position change (standing vs. seated) when compared to the SRM power meter. ...
Article
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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).
... GXT ended when the cyclists voluntarily stopped or when they could not maintain the set power. As they were elite cyclists and minimal changes to the bike's geometry can negatively affect performance or even lead to injury, participants completed the test using their own bicycles attached to a Cycleops Hammer ergometer (CycleOps, Madisson, Wisconsin, USA), pedalling seated and maintaining the preferred cadence constant during the test [28]. ...
Article
The use of near-infrared spectroscopy could be an interesting alternative to other invasive or expensive methods to estimate the second lactate threshold. Our objective was to compare the intensities of the muscle oxygen saturation breakpoint obtained with the Humon Hex and the second lactate threshold in elite cyclists. Ninety cyclists performed a maximal graded exercise test. Blood capillary lactate was obtained at the end of steps and muscle oxygenation was continuously monitored. There were no differences (p>0.05) between muscle oxygen oxygenation breakpoint and second lactate threshold neither in power nor in heart rate, nor when these values were relativized as a percentage of maximal aerobic power or maximum heart rate. There were also no differences when men and women were studied separately. Both methods showed a highly correlation in power (r=0.914), percentage of maximal aerobic power (r=0.752), heart rate (r=0.955), and percentage of maximum heart rate (r=0.903). Bland-Altman resulted in a mean difference of 0.05±0.27 W·kg–1, 0.91±4.93%, 0.63±3.25 bpm, and 0.32±1.69% for power, percentage of maximal aerobic power, heart rate and percentage of maximum heart rate respectively. These findings suggest that Humon may be a non-invasive and low-cost alternative to estimate the second lactate threshold intensity in elite cyclists.
Article
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Purpose: To analyze the relationship between critical power (CP) and different lactate threshold (LT2) markers in cyclists. Methods: Seventeen male recreational cyclists [33 ± 5 years, peak power output (PO) = 4.5 ± 0.7 W/kg] were included in the study. The PO associated with four different fixed (onset of blood lactate accumulation) and individualized (Dmax exp , Dmax pol , and LT Δ1 ) LT2 markers was determined during a maximal incremental cycling test, and CP was calculated from three trials of 1-, 5-, and 20-min duration. The relationship and agreement between each LT2 marker and CP were then analyzed. Results: Strong correlations ( r = 0.81–0.98 for all markers) and trivial-to-small non-significant differences (Hedges’ g = 0.01–0.17, bias = 1–9 W, and p > 0.05) were found between all LT2 markers and CP with the exception of Dmax exp , which showed the strongest correlation but was slightly higher than the CP (Hedges’ g = 0.43, bias = 20 W, and p < 0.001). Wide limits of agreement (LoA) were, however, found for all LT2 markers compared with CP (from ±22 W for Dmax exp to ±52 W for Dmax pol ), and unclear to most likely practically meaningful differences (PO differences between markers >1%, albeit <5%) were found between markers attending to magnitude-based inferences. Conclusion: LT2 markers show a strong association and overall trivial-to-small differences with CP. Nevertheless, given the wide LoA and the likelihood of potentially meaningful differences between these endurance-related markers, caution should be employed when using them interchangeably.
Article
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Purpose: To compare endurance, strength and body composition indicators between cyclists of three different competition age categories. Methods: Fifty-one male road cyclists classified as either junior ( n = 13, age 16.4 ± 0.5 years), under-23 [(U23), n = 24, 19.2 ± 1.3 years] or professional ( n = 14, 26.1 ± 4.8 years) were studied. Endurance (assessed through a maximal incremental test and an 8-minute time-trial), strength/power (assessed through incremental loading tests for the squat, lunge and hip thrust exercises) and body composition (assessed through dual energy X-ray absorptiometry) were determined on three different testing sessions. Results: U23 and, particularly professional, cyclists attained significantly ( p < 0.05) higher values than juniors for most of the analyzed endurance indicators [time-trial performance, maximum oxygen uptake (VO 2max ), peak power output (PPO), respiratory compensation point (RCP), and ventilatory threshold (VT)]. Significant differences ( p < 0.05) between U23 and professionals were also found for time-trial performance, PPO and VT, but not for other markers such as VO 2max or RCP. Professional cyclists also showed significantly ( p < 0.05) lower relative fat mass and higher muscle mass levels than U23 and, particularly, juniors. No consistent differences between age categories were found for muscle strength/power indicators. Conclusion: Endurance (particularly time-trial performance, PPO and VT) and body composition (fat and muscle mass) appear as factors that best differentiate between cyclists of different age categories, whereas no consistent differences are found for muscle strength/power. These findings might help in performance prediction and/or talent identification and may aid in guiding coaches in the design of training programs focused on improving those variables that appear more determinant.
Article
There is a lack of research assessing Motion Performance Indicators (MPIs), which have been recently made commercially available. Therefore, this study explored: (1) the influence of incremented exercise on MPIs and; (2) the relationships between MPIs and cycling performance at different intensities during a graded exercise test (GXT) in professional cyclists. Thirty-six professional cyclists performed GXT until exhaustion with their own bikes attached to a cycle ergometer. MPIs were collected using a real-time motion capture system based on inertial measurement units at 100 Hz of sample rate. Data were extracted from intensities of the GXT when lactate thresholds (LT1, LT2) and peak power (POpeak) were determined. Results showed that only Pelvic Angle ( p < 0.01, d > 1.15) and Pelvic Rotation ( p < 0.01, d > 1.37) were sensitive to increases in exercise intensity (i.e. greater inclination and increased rotation at greater power). Multivariate liner regression analyses showed that a reduced range of movement (ROM) for the upper legs at sub-maximum intensities (LT1 and LT2) was associated with greater power production ( r ² > 0.21), whilst a reduced ROM for the right foot was associated with greater POpeak ( r ² = 0.20). In conclusion, changes in movement patterns were limited to a greater inclination and rotation of the pelvis at maximum power without changes in other MPIs throughout the GXT. Cyclists who produced greater power presented less ROM for their upper legs at LT1 and LT2 whilst at POpeak and greater power production was moderately associated with less ROM for the right foot. Coaches may be able to use MPI to analyze for excess ROM, particularly at higher exercise intensities, as this seems to increase inefficiencies and limit power production.
Article
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We assessed the effects of a short-term velocity-based resistance training (VBRT, where exercise intensity is individualized based on the loads and repetitions that maximize power output) program compared with traditional resistance training (TRT, where the same number of repetitions and relative load are used for every individual) on body composition, muscle strength/power, and endurance performance in competitive female cyclists. Seventeen participants were randomly assigned to 6 weeks (two sessions/week) of TRT (n = 8) or VBRT (n = 9), during which they maintained their usual endurance program. Both interventions included squat, hip thrust, and split squat exercises. Training loads were continuously registered, and outcomes were measures of muscle strength/power, body composition, and endurance performance (incremental test and 8-min time trial). No differences between TRT and VBRT groups were found for overall internal training loads during resistance training or cycling sessions (p > 0.05). Both interventions led to significant improvements in all strength/power-related outcomes, but VBRT induced greater improvements than TRT in maximum muscle strength and power as assessed with the hip thrust exercise (p < 0.05 for the group by time interaction effect). However, no significant group by time interaction effect was found for body composition or endurance performance-related outcomes. In conclusion, the addition of a short-term intervention of VBRT or TRT to the usual training regimen of competitive female cyclists improves muscle strength/power, albeit VBRT might induce superior gains on maximum strength/power for the hip thrust exercise.
Article
Background: The evaluation of performance in endurance athletes and the subsequent individualization of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these methodologies are expensive and invasive. The short-term scaling exponent alpha 1 of detrended Fluctuation Analysis (DFA-α1) of the Heart Rate Variability (HRV) has been proposed as a non-invasive methodology to detect intensity thresholds. Purpose: The aim of this study is to analyse the validity of DFA-α1 HRV analysis to determine the individual training thresholds in elite cyclists and to compare them against the lactate thresholds. Methodology: 38 male elite cyclists performed a graded exercise test to determine their individual thresholds. HRV and blood lactate were monitored during the test. The first (LT1 and DFA-α1-0.75, for lactate and HRV, respectively) and second (LT2 and DFA-α1-0.5, for lactate and HRV, respectively) training intensity thresholds were calculated. Then, these points were matched to their respective power output (PO) and heart rate (HR). Results: There were no significant differences (p > 0.05) between the DFA-α1-0.75 and LT1 with significant positive correlations in PO (r = 0.85) and HR (r = 0.66). The DFA-α1-0.5 was different against LT2 in PO (p = 0.04) and HR (p = 0.02), but it showed significant positive correlation in PO (r = 0.93) and HR (r = 0.71). Conclusions: The DFA1-a-0.75 can be used to estimate LT1 non-invasively in elite cyclists. Further research should explore the validity of DFA-α1-0.5.
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Three to 5 cycling tests to exhaustion allow prediction of time to exhaustion (TTE) at power output based on calculation of critical power (CP). We aimed to determine the accuracy of CP predictions of TTE at power outputs habitually endured by cyclists. Fourteen endurance-trained male cyclists underwent 4 randomized cycle-ergometer TTE tests at power outputs eliciting (i) mean Wingate anaerobic test (WAnT mean ), (ii) maximal oxygen consumption, (iii) respiratory compensation threshold (VT 2 ), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of 5%–9%. Power outputs were 710 ± 63 W for WAnT mean , 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT 2 and 247 ± 20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P < 0.001). The CP predictions overestimated TTE at WAnT mean (00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT 2 (–04:18 ± 03:20 mm:ss; P < 0.05), and correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or lower power outputs (e.g., sprints and 40-km time trials). Novelty CP calculation enables to predict TTE at any cycling power output. We tested those predictions against measured TTE in a wide range of cycling power outputs. CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.
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Lillo-Beviá, JR, Courel-Ibáñez, J, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, and Pallarés, JG. Is the functional threshold power a valid metric to estimate the maximal lactate steady state in cyclists? J Strength Cond Res XX(X): 000-000, 2019-The aims of this study were to determine (a) the repeatability of a 20-minute time-trial (TT20), (b) the location of the TT20 in relation to the main physiological events of the aerobic-anaerobic transition, and (c) the predictive power of a list of correction factors and linear/multiple regression analysis applied to the TT20 result to estimate the individual maximal lactate steady state (MLSS). Under laboratory conditions, 11 trained male cyclists and triathletes (V[Combining Dot Above]O2max 59.7 ± 3.0 ml·kg·min) completed a maximal graded exercise test to record the power output associated with the first and second ventilatory thresholds and V[Combining Dot Above]O2max measured by indirect calorimetry, several 30 minutes constant tests to determine the MLSS, and 2 TT20 tests with a short warm-up. Very high repeatability of TT20 tests was confirmed (standard error of measurement of ±3 W and smallest detectable change of ±9 W). Validity results revealed that MLSS differed substantially from TT20 (bias = 26 ± 7 W). The maximal lactate steady state was then estimated from the traditional 95% factor (bias = 12 ± 7 W) and a novel individual correction factor (ICF% = MLSS/TT20), resulting in 91% (bias = 1 ± 6 W). Complementary linear (MLSS = 0.7488 × TT20 + 43.24; bias = 0 ± 5 W) and multiple regression analysis (bias = 0 ± 4 W) substantially improved the individual MLSS workload estimation. These findings suggest reconsidering the TT20 procedures and calculations to increase the effectiveness of the MLSS prediction.
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Exercise stress testing (EST) is indicated for diagnostic and prognostic purposes in the general population. In athletes, stress tests can also be useful to inform the risk of high-intensity training and competition, to assess athletic conditioning, and to refine training regimens. Many specific indications for EST are unique to athletes. Treadmill and cycle ergometer protocols each have their strengths and disadvantages; extensive protocol customization may be necessary to answer the clinical question at hand. A comprehensive understanding of the available tools for exercise testing, their strengths, and their limitations is crucial to providing cardiovascular care to athletic individuals.
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Garbellotto, L, Petit, E, Brunet, E, Guirronnet, S, Clolus, Y, Gillet, V, Bourdin, H, and Mougin, F. Gradual advance of sleep-wake schedules before an eastward flight and phase adjustment after flight in elite cross-country mountain bikers: effects on sleep and performance. J Strength Cond Res XX(X): 000-000, 2022-Strategies, for alleviating jet lag, specifically targeted to competitive athletes have never been studied, in ecological conditions. This study aimed to assess the effects of a phase advance before a 7-hour eastward flight followed by a strategy of resynchronization at destination on sleep and physical performance in professional mountain bikers. Six athletes participated in this study divided into 4 periods: (i) baseline (usual sleep-wake rhythm); (ii) phase advance (advance sleep-wake schedules of 3 hours for 6 days); (iii) travel (flight: Paris-Tokyo); and (iv) phase adjustment (resynchronization of sleep-wake schedules). Melatonin pills and light therapy were administrated during the phase advance and phase adjustment. Sleep was recorded by polysomnography and actigraphy, core body temperature (CBT) rhythm was assessed by ingestible capsules, and physical performances were tested by the Wingate and 5-minute maximal exercise tests. Results showed that bedtime was advanced by 2.9 hours at the end of the phase advance (p ≤ 0.01) with a batyphase of CBT advanced by 2.5 hours (p = 0.07). Bedtime was similar at destination compared with baseline. Total sleep time and sleep composition were unchanged at the end of the phase advance or at destination, compared with baseline. Physical performances were maintained after phase advance and at destination. The phase advance enabled to preshift part of the time zones without disturbing sleep and physical performances and contributed to preserving them once at destination. A phase advance before eastward travel represents an effective strategy to counter harmful effects of jet lag.
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Purpose: This study aimed to determine the validity, sensitivity, reproducibility and robustness of the Powertap (PWT), Stages (STG) and Garmin Vector (VCT) power meters in comparison with the SRM device. Methods: A national-level male competitive cyclist was required to complete three laboratory cycling tests that included a sub-maximal incremental test, a sub-maximal 30-min continuous test and a sprint test. Two additional tests were performed: the first on vibration exposures in the laboratory and the second in the field. Results: The VCT provided a significantly lower 5 s power output (PO) during the sprint test with a low gear ratio compared with the POSRM (-36.9%). The POSTG was significantly lower than the POSRM within the heavy exercise intensity zone (zone 2, -5.1%) and the low part of the severe intensity zone (zone 3, -4.9%). The POVCT was significantly lower than the POSRM only within zone 2 (-4.5%). The POSTG was significantly lower in standing position than in the seated position (-4.4%). The reproducibility of the PWT, STG and VCT was similar to that of the SRM system. The POSTG and POVCT were significantly decreased from a vibration frequency of 48 Hz and 52 Hz, respectively. Conclusions: The PWT, STG and the VCT systems appear to be reproducible, but the validity, sensitivity and robustness of the STG and VCT systems should be treated with some caution according to the conditions of measurement.
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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.
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The purpose of this study was to compare the pedalling technique in road cyclists of different competitive levels. Eleven professional, thirteen elite and fourteen club cyclists were assessed at the beginning of their competition season. Cyclists’ anthropometric characteristics and bike measurements were recorded. Three sets of pedalling (200, 250 and 300 W) on a cycle ergometer that simulated their habitual cycling posture were performed at a constant cadence (~90 rpm), while kinetic and kinematic variables were registered. The results showed no differences on the main anthropometric variables and bike measurements. Professional cyclists obtained higher positive impulse proportion (1.5–3.3% and P < 0.05), mainly due to a lower resistive torque during the upstroke (15.4–28.7% and P < 0.05). They also showed a higher ankle range of movement (ROM, 1.1–4.0° and P < 0.05). Significant correlations (P < 0.05) were found between the cyclists’ body mass and the kinetic variables of pedalling: positive impulse proportion (r = −0.59 to −0.61), minimum (r = −0.59 to −0.63) and maximum torques (r = 0.35–0.47). In conclusion, professional cyclists had better pedalling technique than elite and club cyclists, because they opted for enhancing pulling force at the recovery phase to sustain the same power output. This technique depended on cycling experience and level of expertise
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This paper provides measurements of rider-induced loads during standing cycling. Two strain gauge dynamometers were used to measure these loads while three subjects rode bicycles on a large motorized treadmill; the cycling situation simulated hill climbing while standing. Comparing the results to those previously published for seated cycling revealed that the loading for standing cycling differed fundamentally from that for seated cycling in certain key respects. One respect was that the maximum magnitude normal pedal force reached substantially higher values, exceeding the weight of the subject, and the phase occurred later in the crank cycle. Another respect was that the direction of the handlebar forces alternated indicating that the arms pulled up and back during the power stroke of the corresponding leg and pushed down and forward during the upstroke. Inasmuch as these forces were coordinated (i.e., in phase) with the leaning of the bicycle, the arms developed positive power.
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This investigation sought to determine if cycling power could be accurately modeled. A mathematical model of cycling power was derived, and values for each model parameter were determined. A bicycle-mounted power measurement system was validated by comparison with a laboratory ergometer. Power was measured during road cycling, and the measured values were compared with the values predicted by the model. The measured values for power were highly correlated (R2 = .97) with, and were not different than, the modeled values. The standard error between the modeled and measured power (2.7 W) was very small. The model was also used to estimate the effects of changes in several model parameters on cycling velocity. Over the range of parameter values evaluated, velocity varied linearly (R2 > .99). The results demonstrated that cycling power can be accurately predicted by a mathematical model.
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Cycling can be performed on the road or indoors on stationary ergometers. The purpose of this study was to investigate differences in cycling efficiency, muscle activity and pedal forces during cycling on a stationary turbo trainer compared with a treadmill. 19 male cyclists cycled on a stationary turbo trainer and on a treadmill at 150, 200 and 250 W. Cycling efficiency was determined using the Douglas bags, muscle activity patterns were determined using surface electromyography and pedal forces were recorded with instrumented pedals. Treadmill cycling induced a larger muscular contribution from Gastrocnemius Lateralis, Biceps Femoris and Gluteus Maximus of respectively 14%, 19% and 10% compared with turbo trainer cycling (p<0.05). Conversely, Turbo trainer cycling induced larger muscular contribution from Vastus Lateralis, Rectus Femoris and Tibialis Anterior of respectively 7%, 17% and 14% compared with treadmill cycling (p<0.05). The alterations in muscle activity resulted in a better distribution of power during the pedal revolution, as determined by an increased Dead Centre size (p<0.05). Despite the alterations in muscle activity and pedalling technique, no difference in efficiency between treadmill (18.8±0.7%) and turbo trainer (18.5±0.6%) cycling was observed. These results suggest that cycling technique and type of ergometer can be altered without affecting cycling efficiency.
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This study assessed the reliability and validity of the Velotron Racermate™ cycle ergometer to assess anaerobic power. Men (9 cyclists and 13 recreationally-active) and women (17 recreationally-active and 1 cyclist) (age=24.7±4.2 yr) performed 2 Wingate tests on the Velotron or 3 Wingate tests (2 on the Velotron and 1 on the Monark Peak Bike) over a 7-14 day period. Peak power, mean power, minimum power, fatigue index, heart rate, and peak and minimum cadence were assessed. Results revealed significant test-retest reliability for mean power (r=0.90, p<0.01), minimum power (r=0.79, p<0.05) and peak power (r=0.70, p<0.05) with repeated bouts on the Velotron. Peak power was significantly higher (p<0.05) on the Velotron (9.95±1.39 W/kg) vs. the Monark (9.13±1.26 W/kg); however, mean power was higher (p<0.05) on the Monark (6.95±0.89 W/kg) vs. the Velotron (6.11±0.52 W/kg and 6.25±0.59 W/kg). Data reveal significant reliability for mean and peak power from the Velotron Racermate, yet multiple variables differ between the Velotron and the Monark mechanically-braked cycle ergometer.
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The purpose of this study was to assess the validity and reliability of the Wattbike cycle ergometer against the SRM Powermeter using a dynamic calibration rig (CALRIG) and trained and untrained human participants. Using the CALRIG power outputs of 50-1 250  W were assessed at cadences of 70 and 90  rev x min(-1). Validity and reliability data were also obtained from 3 repeated trials in both trained and untrained populations. 4 work rates were used during each trial ranging from 50-300  W. CALRIG data demonstrated significant differences (P<0.05) between SRM and Wattbike across the work rates at both cadences. Significant differences existed in recorded power outputs from the SRM and Wattbike during steady state trials (power outputs 50-300  W) in both human populations (156±72  W vs. 153±64  W for SRM and Wattbike respectively; P<0.05). The reliability (CV) of the Wattbike in the untrained population was 6.7% (95%CI 4.8-13.2%) compared to 2.2% with the SRM (95%CI 1.5-4.1%). In the trained population the Wattbike CV was 2.6% (95%CI 1.8-5.1%) compared to 1.1% with the SRM (95%CI 0.7-2.0%). These results suggest that when compared to the SRM, the Wattbike has acceptable accuracy. Reliability data suggest coaches and cyclists may need to use some caution when using the Wattbike at low power outputs in a test-retest setting.
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The ErgomoPro (EP) is a power meter that measures power output (PO) during outdoor and indoor cycling via 2 optoelectronic sensors located in the bottom bracket axis. The aim of this study was to determine the validity and the reproducibility of the EP compared with the SRM crank set and Powertap hub (PT). The validity of the EP was tested in the laboratory during 8 submaximal incremental tests (PO: 100 to 400 W), eight 30-min submaximal constant-power tests (PO = 180 W), and 8 sprint tests (PO > 750 W) and in the field during 8 training sessions (time: 181 +/- 73 min; PO: approximately 140 to 160 W). The reproducibility was assessed by calculating the coefficient of PO variation (CV) during the submaximal incremental and constant tests. The EP provided a significantly higher PO than the SRM and PT during the submaximal incremental test: The mean PO differences were +6.3% +/- 2.5% and +11.1% +/- 2.1% respectively. The difference was greater during field training sessions (+12.0% +/- 5.7% and +16.5% +/- 5.9%) but lower during sprint tests (+1.6% +/- 2.5% and +3.2% +/- 2.7%). The reproducibility of the EP is lower than those of the SRM and PT (CV = 4.1% +/- 1.8%, 1.9% +/- 0.4%, and 2.1% +/- 0.8%, respectively). The EP power meter appears less valid and reliable than the SRM and PT systems.
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Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
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The aim of this study was to test the transferability of workload measurements between three different types of bicycle ergometer. Two common ergometers (Lode Excalibur and Avantronic Cyclus 2) were compared with a powermeter (Schoberer SRM system) that enables the measurement of power output during road cycling. Twelve well-trained subjects participated in this study. Within 12 h, each subject carried out three separate graded incremental exercise tests on each of the ergometric devices, and their oxygen uptake (VO2) and heart rate were determined. The three test protocols were identical: after warm-up, four stages of 4 min each at exercise intensities of 100, 150, 200, and 250 W. Pedalling frequency was controlled and there was no difference between the three ergometers. Tests were administered in a random order. Neither VO2 nor heart rate was affected by the type of ergometer used. For a given intensity, the same values were found in the two laboratory tests and in the field test (VO2: P = 0.425; heart rate: P = 0.845). Thus, the transferability of workload measurements between two different laboratory cycling ergometers and an ambulatory device was proven. Equivalency was determined using VO2 and heart rate as indices of metabolic and cardiovascular strain, respectively.
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Performance tests are an integral component of assessment for competitive cyclists in practical and research settings. Cycle ergometry is the basis of most of these tests. Most cycle ergometers are stationary devices that measure power while a cyclist pedals against sliding friction (e.g. Monark), electromagnetic braking (e.g. Lode), or air resistance (e.g. Kingcycle). Mobile ergometers (e.g. SRM cranks) allow measurement of power through the drive train of the cyclist’s own bike in real or simulated competitions on the road, in a velodrome or in the laboratory. The manufacturers’ calibration of all ergometers is questionable; dynamic recalibration with a special rig is therefore desirable for comparison of cyclists tested on different ergometers. For monitoring changes in performance of a cyclist, an ergometer should introduce negligible random error (variation) in its measurements; in this respect, SRM cranks appear to be the best ergometer, but more comparison studies of ergometers are needed. Random error in the cyclist’s performance should also be minimised by choice of an appropriate type of test. Tests based on physiological measures (e.g. maximum oxygen uptake, anaerobic threshold) and tests requiring self-selection of pace (e.g. constant-duration and constant-distance tests) usually produce random error of at least ~2 to 3%in the measure of power output. Random error as low as ~1% is possible for measures of power in ’all-out’ sprints, incremental tests, constant-power tests to exhaustion and probably also time trials in an indoor velodrome. Measures with such low error might be suitable for tracking the small changes in competitive performance that matter to elite cyclists.
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The SRM power measuring crank system is nowadays a popular device for cycling power output (PO) measurements in the field and in laboratories. The PowerTap (CycleOps, Madison, USA) is a more recent and less well-known device that allows mobile PO measurements of cycling via the rear wheel hub. The aim of this study is to test the validity and reliability of the PowerTap by comparing it with the most accurate (i.e. the scientific model) of the SRM system. The validity of the PowerTap is tested during i) sub-maximal incremental intensities (ranging from 100 to 420 W) on a treadmill with different pedalling cadences (45 to 120 rpm) and cycling positions (standing and seated) on different grades, ii) a continuous sub-maximal intensity lasting 30 min, iii) a maximal intensity (8-s sprint), and iiii) real road cycling. The reliability is assessed by repeating ten times the sub-maximal incremental and continuous tests. The results show a good validity of the PowerTap during sub-maximal intensities between 100 and 450 W (mean PO difference -1.2 +/- 1.3 %) when it is compared to the scientific SRM model, but less validity for the maximal PO during sprint exercise, where the validity appears to depend on the gear ratio. The reliability of the PowerTap during the sub-maximal intensities is similar to the scientific SRM model (the coefficient of variation is respectively 0.9 to 2.9 % and 0.7 to 2.1 % for PowerTap and SRM). The PowerTap must be considered as a suitable device for PO measurements during sub-maximal real road cycling and in sub-maximal laboratory tests.
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The aim of this investigation was to assess the validity and reliability of the Ergomopro powermeter. Nine participants completed trials on a Monark ergometer fitted with Ergomopro and SRM powermeters simultaneously recording power output. Each participant completed multiple trials at power outputs ranging from 50 to 450 W. The work stages recorded were 60 s in duration and were repeated three times. Participants also completed a single trial on a cycle ergometer designed to assess bilateral contributions to work output (Lode Excaliber Sport PFM). The power output during the trials was significantly different between all three systems, (p < 0.01) 231.2 +/- 114.2 W, 233.0 +/- 112.4 W, 227.8 +/- 108.8 W for the Monark, SRM and Ergomopro system, respectively. When the bilateral contributions were factored into the analysis, there were no significant differences between the powermeters (p = 0.58). The reliability of the Ergomopro system (CV%) was 2.31 % (95 % CI 2.13 - 2.52 %) compared to 1.59 % (95 % CI 1.47 to 1.74 %) for the Monark, and 1.37 % (95 % CI 1.26 - 1.50 %) for the SRM powermeter. These results indicate that the Ergomopro system has acceptable accuracy under these conditions. However, based on the reliability data, the increased variability of the Ergomopro system and bilateral balance issues have to be considered when using this device.
Article
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.
Purpose: The purpose of this study was to assess the validity of power output settings of the Wahoo KICKR Power Trainer (KICKR) using a dynamic calibration rig (CALRIG) over a range of power outputs and cadences. Methods: Using the KICKR to set power outputs, powers of 100-999W were assessed at cadences (controlled by the CALRIG) of 80, 90, 100, 110 and 120rpm. Results: The KICKR displayed accurate measurements of power between 250-700W at cadences of 80-120rpm with a bias of -1.1% (95%LoA: -3.6-1.4%). A larger mean bias in power were observed across the full range of power tested, 100-999W 4.2% (95%LoA: -20.1-28.6%), due to larger biases between 100-200W and 750-999W (4.5%, 95%LoA:-2.3-11.3% and 13.0%, 95%LoA: -24.4-50.3%), respectively. Conclusion: When compared to a CALRIG, the Wahoo KICKR Power Trainer has acceptable accuracy reporting a small mean bias and narrow limits of agreement in the measurement of power output between 250-700W at cadences of 80-120rpm. Caution should be applied by coaches and sports scientists when using the KICKR at power outputs of <200W and >750W due to the greater variability in recorded power.
Article
Unexplored in scientific literature, Q Factor describes the horizontal width between bicycle pedals and determines where the foot is laterally positioned throughout the pedal stroke. The aim of the study was to determine whether changing Q Factor has a beneficial effect upon cycling efficiency and muscular activation. A total of 24 trained cyclists (11 men, 13 women; VO(2max) 57.5 ml·kg/min ± 6.1) pedaled at 60% of peak power output for 5 min at 90 rpm using Q Factors of 90, 120, 150, and 180 mm. Power output and gas were collected and muscular activity of the gastrocnemius medialis (GM), tibialis anterior (TA), vastus medialis (VM), and vastus lateralis (VL) measured using surface electromyography. There was a significant increase (P < 0.006) in gross mechanical efficiency (GME) for 90 and 120 mm (both 19.38%) compared with 150 and 180 mm (19.09% and 19.05%), representing an increase in external mechanical work performed of approximately 4-5 W (1.5-2.0%) at submaximal power outputs. There was no significant difference in the level of activity or timing of activation of the GM, TA, VM, and VL between Q Factors. Other muscles used in cycling, and possibly an improved application of force during the pedal stroke may play a role in the observed increase in GME with narrower Q Factors.
Article
This study examined the reliability/validity of power output measured using the Fortius Virtual Reality cycle trainer. 10 cyclists (age: 28±6 years; V˙O (2)max: 60.9±7.2 ml · kg (-1) · min (-1); peak power: 393±82 W) completed three 20 km time trials on a Fortius cycle trainer. During each time trial, power output was measured at 1 Hz using the Fortius internal software and a PowerTap power monitor. Validity calculated for the Fortius trainer; Pearson correlation coefficient (r=0.99; 95% CI: 0.98-0.99; p<0.01) and typical error of estimate (3.5%; 95% CI: 3.2-3.9%), was similar to other established laboratory ergometers. No differences (F (2,16)=0.32; p=0.73) in mean 20 km power were observed between trial 1 (253±46 W), 2 (258±49 W), or 3 (255±50 W). Test-retest reliability (intraclass correlation coefficient (ICC) and coefficient of variation (CV)) was better between trial 2 and 3 (ICC=1.00 (CI: 0.98-1.00); CV: 1.6% (CI: 1.1-3.3%)) compared with trial 1 and 2 (ICC=0.98 (CI: 0.91-1.00); CV: 3.3% (CI: 2.2-6.4%)). The Fortius cycle trainer is a valid and reliable device for the measurement of power output in cyclists, thus providing an alternative to larger more expensive laboratory ergometers.
Article
The purpose of this study was to determine the accuracy of the Velotron cycle ergometer and the SRM power meter using a dynamic calibration rig over a range of exercise protocols commonly applied in laboratory settings. These trials included two sustained constant power trials (250 W and 414 W), two incremental power trials and three high-intensity interval power trials. To further compare the two systems, 15 subjects performed three dynamic 30 km performance time trials. The Velotron and SRM displayed accurate measurements of power during both constant power trials (<1% error). However, during high-intensity interval trials the Velotron and SRM were found to be less accurate (3.0%, CI=1.6-4.5% and -2.6%, CI=-3.2--2.0% error, respectively). During the dynamic 30 km time trials, power measured by the Velotron was 3.7+/-1.9% (CI=2.9-4.8%) greater than that measured by the SRM. In conclusion, the accuracy of the Velotron cycle ergometer and the SRM power meter appears to be dependent on the type of test being performed. Furthermore, as each power monitoring system measures power at various positions (i.e. bottom bracket vs. rear wheel), caution should be taken when comparing power across the two systems, particularly when power is variable.
Article
The calibration of cycle ergometers should be checked regularly. Some studies have shown calibration errors of more than 40%. A simple, inexpensive calibrating method for mechanically braked cycle ergometers was developed and tried out on a new type of ergocycle. The cycle ergometer was elevated and the crank replaced by a pulley fitted to the shaft. The crank speed (rpm) increased linearly as a function of time when different masses were applied on the pulley. For a given braking force on the cycle ergometer, different accelerations corresponding to the increased pulley forces could be measured. When extrapolating for zero acceleration, it was possible to determine a "limit-force" which allowed the system to be in equilibrium. Additional force creates motion. The same experiments were repeated with increasing braking forces. Using the differently sized gear sprockets of the transmission system, it was possible to calculate the actual force, including all the resistances. The actual force found by the calibrating method was then compared with the indicated force proposed by the manufacturer. With increasing forces, the relative errors decreased from 9.6 to 2.9%. The cycle ergometer calibrated by this technique meets the standards recommended in exercise physiology.
In this study we measured the accuracy of the following types of cycle ergometer against the criterion of a dynamic calibration rig (DCR): 35 friction-braked (Monark), 5 research-grade air-braked (Repco) and 5 electromagnetically braked (2 Siemens, 1 Elema-Schonander, 1 Ergoline, 1 Warren E. Collins). Monark ergometer power outputs over the range 58.9-353.2 W significantly (P < 0.001) underestimated those registered by the DCR with mean accuracies of 91.7-97.8%. The least accurate individual reading for each of the six up-scale (0-353.2 W) power outputs ranged from 81.6 to 91.6%; corresponding down-scale (353.2-0 W) accuracies were 85.1-92.5%. A hysteresis effect was furthermore evident for this ergometer in that up-scale measurements were significantly (P < 0.05) greater than down-scale ones. In addition, when the oldest [mean (SD): 11.3 (2.3) years old] and newest [1.4 (0.8) years old] eight ergometers were compared, the latter were significantly (P < 0.05) more accurate over the range 117.7-294.3 W. Apart from the two lowest power outputs of 47 W (62.2-96.0% accuracy) and 127 W (88.0-97.7% accuracy), the individual up-scale and down-scale accuracies of the Repco ergometers ranged from 98.0 to 104.2% for power outputs of 272.7-1137.8 W and the means were not significantly different from those of the DCR. There was also no evidence of hysteresis. Except for the initial power output of 50 W (40 rev/min: 83.8-99.2% accuracy; 60 rev/min: 93.2-122.6% accuracy), the individual accuracies of the electromagnetically braked ergometers ranged from 89.3 to 101.4% over the up-scale range of 100-400 W, and none of the means were significantly different from those of the DCR. The variability of individual errors for the preceding data emphasises that all cycle ergometers should be validated against the criterion of a DCR if accurate power outputs are required.
Article
The purpose of this study was to assess research aimed at measuring performance enhancements that affect success of individual elite athletes in competitive events. Simulations show that the smallest worthwhile enhancement of performance for an athlete in an international event is 0.7-0.4 of the typical within-athlete random variation in performance between events. Using change in performance in events as the outcome measure in a crossover study, researchers could delimit such enhancements with a sample of 16-65 athletes, or with 65-260 in a fully controlled study. Sample size for a study using a valid laboratory or field test is proportional to the square of the within-athlete variation in performance in the test relative to the event; estimates of these variations are therefore crucial and should be determined by repeated-measures analysis of data from reliability studies for the test and event. Enhancements in test and event may differ when factors that affect performance differ between test and event; overall effects of these factors can be determined with a validity study that combines reliability data for test and event. A test should be used only if it is valid, more reliable than the event, allows estimation of performance enhancement in the event, and if the subjects replicate their usual training and dietary practices for the study; otherwise the event itself provides the only dependable estimate of performance enhancement. Publication of enhancement as a percent change with confidence limits along with an analysis for individual differences will make the study more applicable to athletes. Outcomes can be generalized only to athletes with abilities and practices represented in the study. estimates of enhancement of performance in laboratory or field tests in most previous studies may not apply to elite athletes in competitive events.
Article
Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.
Article
Reliability refers to the reproducibility of values of a test, assay or other measurement in repeated trials on the same individuals. Better reliability implies better precision of single measurements and better tracking of changes in measurements in research or practical settings. The main measures of reliability are within-subject random variation, systematic change in the mean, and retest correlation. A simple, adaptable form of within-subject variation is the typical (standard) error of measurement: the standard deviation of an individual's repeated measurements. For many measurements in sports medicine and science, the typical error is best expressed as a coefficient of variation (percentage of the mean). A biased, more limited form of within-subject variation is the limits of agreement: the 95% likely range of change of an individual's measurements between 2 trials. Systematic changes in the mean of a measure between consecutive trials represent such effects as learning, motivation or fatigue; these changes need to be eliminated from estimates of within-subject variation. Retest correlation is difficult to interpret, mainly because its value is sensitive to the heterogeneity of the sample of participants. Uses of reliability include decision-making when monitoring individuals, comparison of tests or equipment, estimation of sample size in experiments and estimation of the magnitude of individual differences in the response to a treatment. Reasonable precision for estimates of reliability requires approximately 50 study participants and at least 3 trials. Studies aimed at assessing variation in reliability between tests or equipment require complex designs and analyses that researchers seldom perform correctly. A wider understanding of reliability and adoption of the typical error as the standard measure of reliability would improve the assessment of tests and equipment in our disciplines.
Article
To assess i) the reproducibility of peak power output recorded during a maximal aerobic power test (MAP), and ii) its validity to predict endurance performance during a field based 16.1-km time trial (16.1-km TT). Two studies were completed: for part I, nine subjects performed three MAP tests; for part II, 16 subjects completed a MAP test and 16.1-km TT. Power output was recorded using an SRM power meter and was calculated as peak power output (PPO) recorded during 60 s of MAP and mean power output for the 16.1-km TT (16.1-km TT(PO)). There was no difference between PPO recorded during the three MAP trials, mean coefficient of variation for individual cyclists was 1.32% (95%CI = 0.97-2.03), and test-retest reliability expressed as an intraclass correlation coefficient was 0.99 (95%CI = 0.96-1.00). A highly significant relationship was found between PPO and 16.1-km TT(PO) (r = 0.99, P < 0.001) but not for PPO and 16.1-km TT time (r = 0.46. P > 0.05). The results show that PPO affords a valid and reliable measure of endurance performance which can be used to predict average power during a 16.1-km TT but not performance time.
Article
This study was designed to examine the effects of moderate-intensity endurance exercise on cycling performance, gross efficiency, and 30-s sprint power output. Two separate experiments were conducted. After a controlled warm-up, subjects completed as much work as possible in a 5-min performance test (EXP1) or a maximal 30-s sprint test (EXP2). These initial exercise bouts were followed by approximately 60 min of cycling at approximately 60% VO2peak or an equivalent period of rest (control) before repeating the warm-up exercise and either the 5-min performance or 30-s sprint test. Expired gas for calculation of cycling gross efficiency was collected over the last minute of each warm-up period. Average 5-min performance power output was significantly reduced (12 W) after exercise in EXP1, and in EXP2 both peak and mean power output were significantly lower (26 and 35 W, respectively). Gross efficiency decreased significantly with exercise in both EXP1 and EXP2. Moreover, the change in gross efficiency was correlated with the change in 5-min performance (r = 0.91, P < 0.01), but not with the change in mean or peak 30-s sprint power output. After sustained moderate-intensity cycling significant reductions in 5-min performance, gross efficiency and sprint power output were observed in endurance trained cyclists. The reduction in 5-min performance was related to the exercise induced decrease in gross efficiency.
Article
: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions. : First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21 degrees C) and time (1 h at ~300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride. : The mean error scores for SRM and PT factory calibration over a range of 50 - 1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however, power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT. : When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.
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.
  • W M Bertucci
  • F Grappe
  • S Crequy
Bertucci WM, Grappe F, Crequy S. Original characteristics of a new cycle ergometer. Sports Engineering. 2011;13(4):171-179.