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ABSTRACT

This study aimed to assess the relationship between an uphill time-trial (TT) performance and both aerobic and anaerobic parameters obtained from laboratory tests. Fifteen cyclists performed a Wingate anaerobic test, a graded exercise test (GXT) and a field-based 20-min TT with 2.7% mean gradient. After a 5-week non-supervised training period, 10 of them performed a second TT for analysis of pacing reproducibility. Stepwise multiple regressions demonstrated that 91% of TT mean power output variation (W kg?1) could be explained by peak oxygen uptake (ml kg?1.min?1) and the respiratory compensation point (W kg?1), with standardised beta coefficients of 0.64 and 0.39, respectively. The agreement between mean power output and power at respiratory compensation point showed a bias ± random error of 16.2 ± 51.8 W or 5.7 ± 19.7%. One-way repeated-measures analysis of variance revealed a significant effect of the time interval (123.1 ± 8.7; 97.8 ± 1.2 and 94.0 ± 7.2% of mean power output, for epochs 0–2, 2–18 and 18–20 min, respectively; P < 0.001), characterising a positive pacing profile. This study indicates that an uphill, 20-min TT-type performance is correlated to aerobic physiological GXT variables and that cyclists adopt reproducible pacing strategies when they are tested 5 weeks apart (coefficients of variation of 6.3; 1 and 4%, for 0–2, 2–18 and 18–20 min, respectively).  相似文献   
2.
大多数教育工作者都认为问题解决很重要,但是他们对什么是问题解决持有不同观点,在怎样教给学生问题解决的本领上也没有达成一致意见。各种不同观点主要集中于将问题解决视为教育目标、教育方法以及技能训练之间的差异问题上。其一,目标型问题解决不应只限于良构问题解决,而应该延伸到现实问题解决;其二,对初学者来说,方法型问题解决有其明显的局限性,给予这些学习者足够帮助,对于培养他们的问题解决能力具有重要作用;其三,技能型问题解决不应被看作是只在培养专长过程的初期才会出现,而应被视为在系统1模式与系统2模式中平行发展的一个过程。总结以上观点,综合学习设计模式完全可以用来促进此三种类型的问题解决,并其本回答了问题解决最好应该怎样教的问题。  相似文献   
3.
Real‐world phenomena simulation models, which can be used to engage middle‐school students with probability, are described. Links to R instructional material and easy‐to‐use code are provided to facilitate implementation in the classroom.  相似文献   
4.
To assess the effect of carbohydrate and caffeine on gross efficiency (GE), 14 cyclists (V?O2max 57.6 ± 6.3 ml.kg?1.min?1) completed 4 × 2-hour tests at a submaximal exercise intensity (60% Maximal Minute Power). Using a randomized, counter-balanced crossover design, participants consumed a standardised diet in the 3-days preceding each test and subsequently ingested either caffeine (CAF), carbohydrate (CHO), caffeine+carbohydrate (CAF+CHO) or water (W) during exercise whilst GE and plasma glucose were assessed at regular intervals (~30 mins). GE progressively decreased in the W condition but, whilst caffeine had no effect, this was significantly attenuated in both trials that involved carbohydrate feedings (W = ?1.78 ± 0.31%; CHO = ?0.70 ± 0.25%, p = 0.008; CAF+CHO = ?0.63 ± 0.27%, p = 0.023; CAF = ?1.12 ± 0.24%, p = 0.077). Blood glucose levels were significantly higher in carbohydrate ingestion conditions (CHO = 4.79 ± 0.67 mmol·L?1, p < 0.001; CAF+CHO = 5.05 ± 0.81 mmol·L?1, p < 0.001; CAF = 4.46 ± 0.75 mmol·L?1; W = 4.20 ± 0.53 mmol·L?1). Carbohydrate ingestion has a small but significant effect on exercise-induced reductions in GE, indicating that cyclists’ feeding strategy should be carefully monitored prior to and during assessment.  相似文献   
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6.
ABSTRACT

Mobile power meters provide a valid means of measuring cyclists’ power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist’s training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session’s variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists’ modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.  相似文献   
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