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Andrea Nicolò, Ilenia Bazzucchi, and Massimo Sacchetti

Purpose:

To verify the accuracy of predicting performance in the severe-intensity domain by means of end-test power output (EP) and the work performed above EP (WEP) obtained from a 3-min all-out test in competitive cyclists.

Methods:

Ten welltrained cyclists performed a ramp incremental test and a 3-min all-out familiarization test. Subsequently, they performed a 3-min all-out experimental test to obtain EP and WEP and a 10-min time trial (TT). The actual 10-min-TT mean power output was then compared with the power output predicted as P = WEP/T lim + EP, where T lim corresponds to 600 s. The ramp-test peak power output (PPO) was compared with PPO predicted as PPO = EP + 2 WEP   S , where S represents the ramp slope (0.5 W/s).

Results:

The actual (347 ± 30 W) and predicted (376 ± 48 W) 10-min TT mean power output were correlated (r = .87, P = .001) but significantly different (P < .01). The coefficient of variation (CV) between the predicted and actual performance was 5.6% ± 4.4%. The error of prediction was positively correlated to EP (r = .80, P = .005) and negatively correlated to WEP (r = –.71, P = .021). No significant difference was found between the 10-min-TT mean power output and EP (351 ± 53 W). The actual (438 ± 30 W) and predicted (472 ± 41 W) ramp PPO were correlated (r = .88, P < .001) but significantly different (P < .001). The CV between the predicted and actual PPO was 5.2% ± 3%. The error of prediction was positively correlated to EP (r = .63, P = .051).

Conclusions:

EP and WEP obtained from a 3-min all-out test overestimate severe-intensity performance in competitive cyclists.

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Len Parker Simpson and Mehdi Kordi

Purpose:

Typically, accessing the asymptote (critical power; CP) and curvature constant (W′) parameters of the hyperbolic power–duration relationship requires multiple constant-power exhaustive-exercise trials spread over several visits. However, more recently single-visit protocols and personal power meters have been used. This study investigated the practicality of using a 2-trial, single-visit protocol in providing reliable CP and W′ estimates.

Methods:

Eight trained cyclists underwent 3- and 12-min maximal-exercise trials in a single session to derive (2-trial) CP and W′ estimates. On a separate occasion a 5-min trial was performed, providing a 3rd trial to calculate (3-trial) CP and W′.

Results:

There were no differences in CP (283 ± 66 vs 282 ± 65 W) or W′ (18.72 ± 6.21 vs 18.27 ± 6.29 kJ) obtained from either the 2-trial or 3-trial method, respectively. After 2 familiarization sessions (completing a 3- and a 12-min trial on both occasions), both CP and W′ remained reliable over additional separate measurements.

Conclusions:

The current study demonstrates that after 2 familiarization sessions, reliable CP and W′ parameters can be obtained from trained cyclists using only 2 maximal-exercise trials. These results offer practitioners a practical, time-efficient solution for incorporating power–duration testing into applied athlete support.

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Dajo Sanders, Mathieu Heijboer, Ibrahim Akubat, Kenneth Meijer, and Matthijs K. Hesselink

Purpose:

To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists.

Methods:

Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model.

Results:

The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model.

Conclusions:

This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.

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Alfred Nimmerichter, Bernhard Prinz, Matthias Gumpenberger, Sebastian Heider, and Klaus Wirth

ventilation) homeostasis, 4 , 5 and the different physiological responses to exercise above versus below CP have been repeatedly demonstrated (for reviews see 6 , 7 ). To estimate CP and W′ , linear and nonlinear 2-parameter models have been used. Traditionally, the power–duration relationship is

Open access

Ryota Akagi, Yuta Nomura, Chiho Kawashima, Mari Ito, Kosuke Oba, Yuma Tsuchiya, Geoffrey A. Power, and Kosuke Hirata

with reference to previously reported power–duration relationships ( Burnley & Jones, 2018 ; Neder et al., 2000 ; Poole et al., 2016 ). It was hypothesized that older people with a higher prefatigue power output have greater neuromuscular fatigue resistance. Materials and Methods Sample Size

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Philip Friere Skiba and David C. Clarke

The power–duration relationship is of interest to athletes, coaches, and sport scientists because it provides insights into the athlete’s physiology and performance capabilities. Traditionally, this relationship is measured using laboratory-based time-to-exhaustion trials of differing durations. In

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Alan Chorley, Richard P. Bott, Simon Marwood, and Kevin L. Lamb

of the power-duration relationship . J Appl Physiol . 2010 ; 108 ( 4 ): 866 – 874 . PubMed ID: 20093659 doi:10.1152/japplphysiol.91425.2008 10.1152/japplphysiol.91425.2008 20093659 8. Vanhatalo A , Fulford J , DiMenna FJ , Jones AM . Influence of hyperoxia on muscle metabolic

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Peter Leo, James Spragg, Iñigo Mujika, Verena Menz, and Justin S. Lawley

practice as an alternative method to laboratory-based testing. 5 In cycling, the power–duration relationship and associated power profiling is used to guide and inform the training process as well as to monitor performance. 6 , 7 Assessments from laboratory tests alone may not provide enough information

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Mehdi Kordi, Campbell Menzies, and Andy Galbraith

Human physiology in linear energetics can be characterized by the hyperbolic power–duration relationship. 1 , 2 Much of the literature in this area has focused on cycling, using power measurements, because it is controlled, accurate, negates environmental conditions, and the mechanical output is

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David Giles, Joel B. Chidley, Nicola Taylor, Ollie Torr, Josh Hadley, Tom Randall, and Simon Fryer

power–duration relationship for all-out exercise . Eur J Appl Physiol . 2012 ; 112 ( 7 ): 2467 – 2473 . doi:10.1007/s00421-011-2214-7 10.1007/s00421-011-2214-7 24. Burnley M , Davison G , Baker JR . Effects of priming exercise on VO 2 kinetics and the power-duration relationship . Med Sci