Laboratory Performance Evaluations, Time Trial Performance, And Training Intensity Distribution In Elite Masters Cyclists

2009 ◽  
Vol 41 ◽  
pp. 461-462
Author(s):  
Adam D. St.Pierre ◽  
Neal A. Henderson
2022 ◽  
Vol 3 ◽  
Author(s):  
Manuel Matzka ◽  
Robert Leppich ◽  
Hans-Christer Holmberg ◽  
Billy Sperlich ◽  
Christoph Zinner

Purpose: To evaluate retrospectively the training intensity distribution (TID) among highly trained canoe sprinters during a single season and to relate TID to changes in performance.Methods: The heart rates during on-water training by 11 German sprint kayakers (7 women, 4 men) and one male canoeist were monitored during preparation periods (PP) 1 and 2, as well as during the period of competition (CP) (total monitoring period: 37 weeks). The zones of training intensity (Z) were defined as Z1 [<80% of peak oxygen consumption (VO2peak)], Z2 (81–87% VO2peak) and Z3 (>87% VO2peak), as determined by 4 × 1,500-m incremental testing on-water. Prior to and after each period, the time required to complete the last 1,500-m stage (all-out) of the incremental test (1,500-m time-trial), velocities associated with 2 and 4 mmol·L−1 blood lactate (v2[BLa], v4[BLa]) and VO2peak were determined.Results: During each period, the mean TID for the entire group was pyramidal (PP1: 84/12/4%, PP2: 80/12/8% and CP: 91/5/4% for Z1, Z2, Z3) and total training time on-water increased from 5.0 ± 0.9 h (PP1) to 6.1 ± 0.9 h (PP2) and 6.5 ± 1.0 h (CP). The individual ranges for Z1, Z2 and Z3 were 61–96, 2–26 and 0–19%. During PP2 VO2peak (25.5 ± 11.4%) markedly increased compared to PP1 and CP and during PP1 v2[bla] (3.6 ± 3.4%) showed greater improvement compared to PP2, but not to CP. All variables related to performance improved as the season progressed, but no other effects were observed. With respect to time-trial performance, the time spent in Z1 (r = 0.66, p = 0.01) and total time in all three zones (r = 0.66, p = 0.01) showed positive correlations, while the time spent in Z2 (r = −0.57, p = 0.04) was negatively correlated.Conclusions: This seasonal analysis of the effects of training revealed extensive inter-individual variability. Overall, TID was pyramidal during the entire period of observation, with a tendency toward improvement in VO2peak, v2[bla], v4[bla] and time-trial performance. During PP2, when the COVID-19 lockdown was in place, the proportion of time spent in Z3 doubled, while that spent in Z1 was lowered; the total time spent training on water increased; these changes may have accentuated the improvement in performance during this period. A further increase in total on-water training time during CP was made possible by reductions in the proportions of time spent in Z2 and Z3, so that more fractions of time was spent in Z1.


2009 ◽  
Vol 4 (3) ◽  
pp. 408-411 ◽  
Author(s):  
Christian Lorenzen ◽  
Morgan D. Williams ◽  
Paul S. Turk ◽  
Daniel L. Meehan ◽  
Daniel J. Cicioni Kolsky

Purpose:Running velocity reached at maximal oxygen uptake (vVO2max) can be a useful measure to prescribe training intensity for aerobic conditioning. Obtaining it in the laboratory is often not practical, and average velocities from time trials are an attractive alternative. To date, the efficacies of such practices for team sport players are unknown. This study aimed to assess the relationship between vVO2max obtained in the laboratory against two time-trial estimates (1500 m and 3200 m).Methods:During the early preseason, elite Australian Rules football players (n = 23, 22.7 ± 3.4 y, 187.7 ± 8.2 cm, 75.5 ± 9.2 kg) participated in a laboratory test on a motorized treadmill and two outdoor time trials.Results:Based on average velocity the 1500-m time-trial performance (5.01 ± 0.23 m·s−1) overestimated (0.36 m·s−1, d = 1.75), whereas the 3200-m time trial (4.47 ± 0.23 m·s−1) underestimated (0.17 m·s−1, d = 0.83) the laboratory vVO2max (4.64 ± 0.18 m·s−1). Despite these differences, both 1500-m and 3200-m time-trial performances correlated with the laboratory measure (r = -0.791; r = -0.793 respectively). Both subsequent linear regressions were of good ft and predicted the laboratory measure within ± 0.12 m·s−1.Conclusion:Estimates of vVO2max should not be used interchangeably, nor should they replace the laboratory measure. When laboratory testing is not accessible for team sports players, prescription of training intensity may be more accurately estimated from linear regression based on either 1500-m or 3200-m time-trial performance than from the corresponding average velocity.


2019 ◽  
Vol 14 (10) ◽  
pp. 1318-1330 ◽  
Author(s):  
Danny Lum ◽  
Tiago M. Barbosa

Purpose: To evaluate the effect of strength training on Olympic time-based sports (OTBS) time-trial performance and provide an estimate of the impact of type of strength training, age, training status, and training duration on OTBS time-trial performance. Methods: A search on 3 electronic databases was conducted. The analysis comprised 32 effects in 28 studies. Posttest time-trial performance of intervention and control group from each study was used to estimate the standardized magnitude of impact of strength training on OTBS time-trial performance. Results: Strength training had a moderate positive effect on OTBS time-trial performance (effect size = 0.59, P < .01). Subgroup meta-analysis showed that heavy weight training (effect size = 0.30, P = .01) produced a significant effect, whereas other modes did not induce significant effects. Training status as factorial covariate was significant for well-trained athletes (effect size = 0.62, P = .04), but not for other training levels. Meta-regression analysis yielded nonsignificant relationship with age of the participants recruited (β = −0.04; 95% confidence interval, −0.08 to 0.004; P = .07) and training duration (β = −0.05; 95% confidence interval, −0.11 to 0.02; P = .15) as continuous covariates. Conclusion: Heavy weight training is an effective method for improving OTBS time-trial performance. Strength training has greatest impact on well-trained athletes regardless of age and training duration.


2008 ◽  
Author(s):  
Charles S. Fulco ◽  
Stephen R. Muza ◽  
Beth Beidleman ◽  
Juli Jones ◽  
Eric Lammi ◽  
...  

2014 ◽  
Vol 28 (9) ◽  
pp. 2513-2520 ◽  
Author(s):  
Renato A.S. Silva ◽  
Fernando L. Silva-Júnior ◽  
Fabiano A. Pinheiro ◽  
Patrícia F.M. Souza ◽  
Daniel A. Boullosa ◽  
...  

2008 ◽  
Vol 26 (14) ◽  
pp. 1477-1487 ◽  
Author(s):  
Marc J. Quod ◽  
David T. Martin ◽  
Paul B. Laursen ◽  
Andrew S. Gardner ◽  
Shona L. Halson ◽  
...  

2010 ◽  
Vol 5 (2) ◽  
pp. 140-151 ◽  
Author(s):  
Mohammed Ihsan ◽  
Grant Landers ◽  
Matthew Brearley ◽  
Peter Peeling

Purpose:The effect of crushed ice ingestion as a precooling method on 40-km cycling time trial (CTT) performance was investigated.Methods:Seven trained male subjects underwent a familiarization trial and two experimental CTT which were preceded by 30 min of either crushed ice ingestion (ICE) or tap water (CON) consumption amounting to 6.8 g⋅kg-1 body mass. The CTT required athletes to complete 1200 kJ of work on a wind-braked cycle ergometer. During the CTT, gastrointestinal (Tgi) and skin (Tsk) temperatures, cycling time, power output, heart rate (HR), blood lactate (BLa), ratings of perceived exertion (RPE) and thermal sensation (RPTS) were measured at set intervals of work.Results:Precooling lowered the Tgi after ICE significantly more than CON (36.74 ± 0.67°C vs 37.27 ± 0.24°C, P < .05). This difference remained evident until 200 kJ of work was completed on the bike (37.43 ± 0.42°C vs 37.64 ± 0.21°C). No significant differences existed between conditions at any time point for Tsk, RPE or HR (P > .05). The CTT completion time was 6.5% faster in ICE when compared with CON (ICE: 5011 ± 810 s, CON: 5359 ± 820 s, P < .05).Conclusions:Crushed ice ingestion was effective in lowering Tgi and improving subsequent 40-km cycling time trial performance. The mechanisms for this enhanced exercise performance remain to be clarified.


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