scholarly journals Lower volume throughout the taper and higher intensity in the last interval session prior to a 1,500 m time trial improves performance

2021 ◽  
Author(s):  
Kate L. Spilsbury ◽  
Barry W Fudge ◽  
Myra A Nimmo ◽  
Steve H. Faulkner
Keyword(s):  
Prediction Models ◽  
Time Trial ◽  
Confidence Limits ◽  
Distance Runners ◽  
Pacing Strategy ◽  
Performance Time ◽  
Lower Volume ◽  
Fast Start ◽  
Regular Training ◽  
Continuous Running

Eight highly-trained middle-distance runners (1,500 m personal best 4:01.4 ± 0:09.2 min) completed two 7-day tapers, separated by at least 3 weeks of regular training: (i) prescribed using prediction models from elite middle-distance runners, where continuous running volume was reduced by 30% and interval intensity was equal to 1,500 m race pace (RP); and (ii) continuous running volume was reduced by 60% and intensity of the final interval session was completed at 110% of 1,500 m race pace (HI). Performance was assessed using 1,500 m time trials on an indoor 200 m track one day before, and one day after each taper. Performance time was improved after HI by 5.2 ± 3.7 s (mean ± 90% confidence limits, p = 0.03) and by 3.2 ± 3.8 s after RP (p = 0.15). The first and second 300-m segments of the 1,500 m time trial were faster post-taper in RP (p = 0.012 and p = 0.017, respectively) and HI (both p = 0.012). Running faster than race pace late in a low-volume taper is recommended to improve 1,500 m track performance. A positive pacing strategy is adopted after tapering, although care should be taken to avoid an over-fast start. Novel Findings • A large reduction in volume during tapering and an increase in final interval session intensity improves running performance • Athletes adopt a negative pacing strategy before tapering and a positive-pacing strategy after tapering

scholarly journals Effects of an increase in intensity during tapering on 1500-m running performance

2019 ◽  
Vol 44 (7) ◽  
pp. 783-790
Author(s):  
Kate L. Spilsbury ◽  
Myra A. Nimmo ◽  
Barry W. Fudge ◽  
Jamie S.M. Pringle ◽  
Mark W. Orme ◽  
...  
Keyword(s):  
Prediction Models ◽  
Training Session ◽  
Time Trial ◽  
Interval Training ◽  
Voluntary Contraction ◽  
Distance Runners ◽  
Running Performance ◽  
Before And After ◽  
Trained Runners ◽  
Regular Training

We examined the effect of completing the final interval training session during a taper at either (i) race pace (RP) or (ii) faster than RP on 1500-m running performance and neuromuscular performance. Ten trained runners (age, 21.7 ± 3.0 years; height, 182.9 ± 7.0 cm; body mass, 73.4 ± 6.8 kg; and personal best 1500-m time, 4:17.5 ± 0:26.9 min) completed 2 conditions consisting of 7 days of regular training and a 7-day taper, separated by 3 weeks of training. In 1 condition, the taper was prescribed using prediction models based on the practices of elite British middle-distance runners, with the intensity of the final interval session being equal to 1500-m RP. The taper was repeated in the high-intensity (HI) condition, with the exception that the final interval session was completed at 115% of 1500-m RP. A 1500-m treadmill time trial and measures of maximal voluntary contraction (MVC) and rate of force development (RFD) were completed before and after regular training and tapering. Performance was most likely improved after RP (mean ± 90% confidence limits, 10.1 ± 1.6 s), and possibly beneficial after HI (4.2 ± 12.0 s). Both MVC force (p = 0.002) and RFD (p = 0.02) were improved after tapering, without differences between conditions. An RP taper based on the practices of elite middle-distance runners is recommended to improve performance in young, subelite runners. The effect of this strategy with an increase in interval intensity is highly variable and should be implemented with caution.

scholarly journals Summated Hazard Score as a Powerful Predictor of Fatigue in Relation to Pacing Strategy

2021 ◽  
Vol 18 (4) ◽  
pp. 1984
Author(s):  
Sylvia Binkley ◽  
Carl Foster ◽  
Cristina Cortis ◽  
Jos J. de Koning ◽  
Christopher Dodge ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Time Trial ◽  
Rating Of Perceived Exertion ◽  
Secondary Outcome ◽  
Outcome Variable ◽  
Start Time ◽  
Slow Start ◽  
Pacing Strategy ◽  
Secondary Outcome Variable ◽  
Fast Start

During competitive events, the pacing strategy depends upon how an athlete feels at a specific moment and the distance remaining. It may be expressed as the Hazard Score (HS) with momentary HS being shown to provide a measure of the likelihood of changing power output (PO) within an event and summated HS as a marker of how difficult an event is likely to be perceived to be. This study aimed to manipulate time trial (TT) starting strategies to establish whether the summated HS, as opposed to momentary HS, will improve understanding of performance during a simulated cycling competition. Seven subjects (peak PO: 286 ± 49.7 W) performed two practice 10-km cycling TTs followed by three 10-km TTs with imposed PO (±5% of mean PO achieved during second practice TT and a self-paced TT). PO, rating of perceived exertion (RPE), lactate, heart rate (HR), HS, summated HS, session RPE (sRPE) were collected. Finishing time and mean PO for self-paced (time: 17.51 ± 1.41 min; PO: 234 ± 62.6 W), fast-start (time: 17.72 ± 1.87 min; PO: 230 ± 62.0 W), and slow-start (time: 17.77 ± 1.74 min; PO: 230 ± 62.7) TT were not different. There was a significant interaction between each secondary outcome variable (PO, RPE, lactate, HR, HS, and summated HS) for starting strategy and distance. The evolution of HS reflected the imposed starting strategy, with a reduction in PO following a fast-start, an increased PO following a slow-start with similar HS during the last part of all TTs. The summated HS was strongly correlated with the sRPE of the TTs (r = 0.88). The summated HS was higher with a fast start, indicating greater effort, with limited time advantage. Thus, the HS appears to regulate both PO within a TT, but also the overall impression of the difficulty of a TT.

Functional Threshold Power Estimated from a 20-minute Time-trial Test is Warm-up-dependent

2021 ◽  
Author(s):  
Artur Ferreira Tramontin ◽  
Fernando Klitzke Borszcz ◽  
Vitor Costa
Keyword(s):  
Time Trial ◽  
The Other ◽  
Threshold Power ◽  
Incremental Test ◽  
Warm Up ◽  
Pacing Strategy ◽  
Trial Test ◽  
Fast Start ◽  
Power Outputs ◽  
Trial Performance

AbstractThis study investigated the influence of different warm-up protocols on functional threshold power. Twenty-one trained cyclists (˙VO2max=60.2±6.8 ml·kg−1·min−1) performed an incremental test and four 20-min time trials preceded by different warm-up protocols. Two warm-up protocols lasted 45 min, with a 5-min time trial performed either 15 min (Traditional) or 25 min (Reverse) before the 20-min time trial. The other two warm-up protocols lasted 25 min (High Revolutions-per minute) and 10 min (Self-selected), including three fast accelerations and self-selected intensity, respectively. The power outputs achieved during the 20-min time trial preceded by the Traditional and Reverse warm-up protocols were significantly lower than the High Revolutions-per-minute and Self-selected protocols (256±30; 257±30; 270±30; 270±30 W, respectively). Participants chose a conservative pacing strategy at the onset (negative) for the Traditional and Reverse but implemented a fast-start strategy (U-shaped) for the High revolutions-per-minute and Self-selected warm-up protocols. In conclusion, 20-min time-trial performance and pacing are affected by different warm-ups. Consequently, the resultant functional threshold power may be different depending on whether the original protocol with a 5-min time trial is followed or not.

Influence of corners and road conditions on cycling individual time trial performance and ‘optimal’ pacing strategy: A simulation study

2020 ◽  
pp. 175433712097487
Author(s):  
Andrea Zignoli
Keyword(s):  
Friction Coefficient ◽  
Time Trial ◽  
Maximal Velocity ◽  
Dynamic Optimisation ◽  
Lateral Dynamics ◽  
Pacing Strategy ◽  
Performance Time ◽  
Road Geometry ◽  
Road Friction ◽  
Road Friction Coefficient

A mathematical model of a bike-rider’s longitudinal and lateral dynamics was used to study the influence of road conditions (tyre-road friction coefficient) on cycling individual time trial (ITT) performance and pacing strategy. A dynamic optimisation approach was used on different simulated 40-km-ITT courses, where environmental variables (i.e. slope and wind), the presence of corners and the tyre-road friction coefficient were varied. The objective of the optimisation was the performance time. Maximal velocity was constrained by road geometry and the tyre-road friction coefficient. The maximal deliverable power output was constrained accordingly to the critical power model. The simulation results suggest that when technical sections constitute 25% of the entire course, road conditions can meaningfully affect the final performance time and peak power required, but not the pacing strategy. In fact, the time lost in slow technical sections cannot be regained during fast straight sections, even if technical sections are used to restore anaerobic energy stores. However, more experimental research is needed to test the applicability of these predictions.

Pacing Strategies of Inexperienced Children During Repeated 800 m Individual Time-Trials and Simulated Competition

2013 ◽  
Vol 25 (2) ◽  
pp. 198-211 ◽  
Author(s):  
Danielle Lambrick ◽  
Alex Rowlands ◽  
Thomas Rowland ◽  
Roger Eston
Keyword(s):  
Perceived Exertion ◽  
Time Trial ◽  
Ratings Of Perceived Exertion ◽  
Pacing Strategy ◽  
Performance Time ◽  
Pacing Strategies ◽  
Graded Exercise ◽  
Laboratory Trial ◽  
Overall Performance ◽  
The Difference

Prior experience of fatiguing tasks is considered essential to establishing an optimal pacing strategy. This study examined the pacing behavior of inexperienced children during self-paced, 800 m running, both individually and within a competitive environment. Thirteen children (aged 9−11 y) completed a graded-exercise test to volitional exhaustion on a treadmill (laboratory trial), followed by three self-paced, individual 800 m time-trials (Trials 1−3) and one self-paced, competitive 800 m time-trial (Trial 4) on an outdoor athletics track. Ratings of perceived exertion (RPE) and heart rate (HR) were measured throughout all trials. Overall performance time improved from Trial 1−3 (250.1 ± 50.4 s & 242.4 ± 51.5 s, respectively, p < .017). The difference in overall performance time between Trials 3 and 4 (260.5 ± 54.2 s) was approaching significance (p = .06). The pacing strategy employed from the outset was consistent across all trials. These findings dispute the notion that an optimal pacing strategy is learned with exercise experience or training.

Anaerobic Speed Reserve: A Key Component of Elite Male 800-m Running

2019 ◽  
Vol 14 (4) ◽  
pp. 501-508 ◽  
Author(s):  
Gareth N. Sandford ◽  
Sian V. Allen ◽  
Andrew E. Kilding ◽  
Angus Ross ◽  
Paul B. Laursen
Keyword(s):  
Confidence Limits ◽  
Performance Time ◽  
Maximal Aerobic Speed ◽  
Practical Tool ◽  
Individualized Training ◽  
The Difference ◽  
Performance Times ◽  
Negative Relationships ◽  
Reserve Ratio ◽  
Race Performance

Purpose: In recent years (2011–2016), men’s 800-m championship running performances have required greater speed than previous eras (2000–2009). The “anaerobic speed reserve” (ASR) may be a key differentiator of this performance, but profiles of elite 800-m runners and their relationship to performance time have yet to be determined. Methods: The ASR—determined as the difference between maximal sprint speed (MSS) and predicted maximal aerobic speed (MAS)—of 19 elite 800- and 1500-m runners was assessed using 50-m sprint and 1500-m race performance times. Profiles of 3 athlete subgroups were examined using cluster analysis and the speed reserve ratio (SRR), defined as MSS/MAS. Results: For the same MAS, MSS and ASR showed very large negative (both r = −.74 ± .30, ±90% confidence limits; very likely) relationships with 800-m performance time. In contrast, for the same MSS, ASR and MAS had small negative relationships (both r = −.16 ± .54; possibly) with 800-m performance. ASR, 800-m personal best, and SRR best defined the 3 subgroups along a continuum of 800-m runners, with SRR values as follows: 400–800 m ≥ 1.58, 800 m ≤ 1.57 to ≥ 1.48, and 800–1500 m ≤ 1.47 to ≥ 1.36. Conclusion: MSS had the strongest relationship with 800-m performance, whereby for the same MSS, MAS and ASR showed only small relationships to differences in 800-m time. Furthermore, the findings support the coaching observation of three 800-m subgroups, with the SRR potentially representing a useful and practical tool for identifying an athlete’s 800-m profile. Future investigations should consider the SRR framework and its application for individualized training approaches in this event.

Effect of environmental feedbacks on pacing strategy and affective load during a self-paced 30 min cycling time trial

2018 ◽  
Vol 37 (3) ◽  
pp. 291-297
Author(s):  
Alexandre Abel ◽  
Bertrand Baron ◽  
Frédéric Grappe ◽  
Marc Francaux
Keyword(s):  
Time Trial ◽  
Cycling Time Trial ◽  
Pacing Strategy ◽  
Cycling Time

scholarly journals The Effect of Dietary Nitrate Supplementation on Physiology and Performance in Trained Cyclists

2017 ◽  
Vol 12 (5) ◽  
pp. 684-689 ◽  
Author(s):  
Joseph A. McQuillan ◽  
Deborah K. Dulson ◽  
Paul B. Laursen ◽  
Andrew E. Kilding
Keyword(s):  
Time Trial ◽  
Crossover Design ◽  
Moderate Intensity ◽  
Beetroot Juice ◽  
Double Blind ◽  
Dietary Nitrate ◽  
Performance Time ◽  
Exercise Economy ◽  
Nitrate Supplementation ◽  
And Performance

Purpose:To determine the effect of dietary nitrate (NO3 –) supplementation on physiology and performance in well-trained cyclists after 6–8 d of NO3 – supplementation.Methods:Eight competitive male cyclists (mean ± SD age 26 ± 8 y, body mass 76.7 ± 6.9 kg, VO2peak 63 ± 4 mL · kg–1 · min–1) participated in a double-blind, placebo-controlled, crossover-design study in which participants ingested 70 mL of beetroot juice containing ~4 mmol NO3 – (NIT) or a NO3 –-depleted placebo (PLA), each for 8 d. Replicating pretreatment measures, participants undertook an incremental ramp assessment to determine VO2peak and first (VT1) and second (VT2) ventilatory thresholds on d 6 (NIT6 and PLA6), moderate-intensity cycling economy on d 7 (NIT7 and PLA7), and a 4-km time trial (TT) on d 8 (NIT8 and PLA8).Results:Relative to PLA, 6 d of NIT supplementation produced unclear effects for VO2peak (mean ± 95% confidence limit: 1.8% ± 5.5%) and VT1 (3.7% ± 12.3%) and trivial effects for both VT2 (–1.0% ± 3.0%) and exercise economy on d 7 (–1.0% ± 1.6%). However, effects for TT performance time (–0.7% ± 0.9%) and power (2.4% ± 2.5%) on d 8 were likely beneficial.Conclusions:Despite mostly unclear outcomes for standard physiological determinants of performance, 8 d of NO3 – supplementation resulted in likely beneficial improvements to 4-km TT performance in well-trained male endurance cyclists.

scholarly journals The Ingestion of 39 or 64 g·hr−1 of Carbohydrate is Equally Effective at Improving Endurance Exercise Performance in Cyclists

2015 ◽  
Vol 25 (3) ◽  
pp. 285-292 ◽  
Author(s):  
Michael L. Newell ◽  
Angus M. Hunter ◽  
Claire Lawrence ◽  
Kevin D. Tipton ◽  
Stuart D. R. Galloway
Keyword(s):  
Power Output ◽  
Statistical Significance ◽  
Time Trial ◽  
Cycling Performance ◽  
Constant Load ◽  
Peak Power Output ◽  
Task Completion ◽  
Intake Rate ◽  
Mean Power ◽  
Pacing Strategy

In an investigator-blind, randomized cross-over design, male cyclists (mean± SD) age 34.0 (± 10.2) years, body mass 74.6 (±7.9) kg, stature 178.3 (±8.0) cm, peak power output (PPO) 393 (±36) W, and VO2max 62 (±9) ml·kg−1min−1 training for more than 6 hr/wk for more than 3y (n = 20) completed four experimental trials. Each trial consisted of a 2-hr constant load ride at 95% of lactate threshold (185 ± 25W) then a work-matched time trial task (~30min at 70% of PPO). Three commercially available carbohydrate (CHO) beverages, plus a control (water), were administered during the 2-hr ride providing 0, 20, 39, or 64g·hr−1 of CHO at a fluid intake rate of 1L·hr−1. Performance was assessed by time to complete the time trial task, mean power output sustained, and pacing strategy used. Mean task completion time (min:sec ± SD) for 39g·hr−1 (34:19.5 ± 03:07.1, p = .006) and 64g·hr−1 (34:11.3 ± 03:08.5 p = .004) of CHO were significantly faster than control (37:01.9 ± 05:35.0). The mean percentage improvement from control was −6.1% (95% CI: −11.3 to −1.0) and −6.5% (95% CI: −11.7 to −1.4) in the 39 and 64g·hr−1 trials respectively. The 20g·hr−1 (35:17.6 ± 04:16.3) treatment did not reach statistical significance compared with control (p = .126) despite a mean improvement of −3.7% (95% CI −8.8−1.5%). No further differences between CHO trials were reported. No interaction between CHO dose and pacing strategy occurred. 39 and 64g·hr−1 of CHO were similarly effective at improving endurance cycling performance compared with a 0g·hr−1 control in our trained cyclists.

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