Performance and physiological effects of different descending strategies for cross‐country mountain biking

Miller, Matthew C.; Macdermid, Paul W.; Fink, Philip W.; Stannard, Stephen R.

doi:10.1080/17461391.2016.1237550

Cited by 14 publications

(10 citation statements)

References 34 publications

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“…In conclusion, the findings of this study confirm past research suggesting that XCO performance is based on a combination of highly developed aerobic and anaerobic capacities as well as skillbased characteristics [14,16,19,20,23]. Nevertheless, the findings suggest that laboratory data may provide an accurate means of modelling cycling performance.…”

Section: Discussionsupporting

confidence: 87%

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes

et al. 2019

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Mountain bike Cross-Country Olympic (XCO) has an intermittent performance profile, underlining the importance of anaerobic metabolism. Traditional performance tests in cycling primarily quantify aerobic metabolism and inadequately meet the demands in XCO. The aim was therefore to validate a specific test that quantifies these requirements by means of an XCO race.Twenty-three competitive XCO athletes (17.9±3.6 years) performed a previously developed performance test and an XCO race within one week. Correlations between individual anaerobic threshold (IAT), 4 mmol lactate threshold (LT4), maximal aerobic power (MAP), maximal effort time trials (TT) for 10–300 s and mean power output of the race (POR) were calculated. In addition, a multiple regression model of the predictive value of the test was calculated.Variables correlated significantly (p<.01) with POR: IAT (r=.81), LT4 (r=0.79), MAP (r=0.91), TT10 (r=0.75), TT30 (r=0.85), TT60 (r=0.84) and TT300 (r=0.86). In the regression model, sex and body mass were set influencing variables (R²adj.=0.70), whereby MAP had the highest correlation with POR and significantly improved the predictive value of the model (R²adj.=0.86).The high correlation of collected performance variables with POR indicated the MTB-PT’s additional benefit for performance testing in XCO because it is specific but very feasible.

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Section: Discussionsupporting

confidence: 87%

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes

et al. 2019

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“…This may be due to the fact that athletes with higher aerobic fitness are affected in a greater extent than lower level athletes [8,10]. Meanwhile, altitude reduces air density, which could have an influence on cycling aerodynamic drag, in particular in flat and downhill terrains [29]. Although caution is required regarding its putative effect [7], with a circuit design, this may favor XCO performance [11], in particular in lower levels groups such as U23 and elite female.…”

Section: Discussionmentioning

confidence: 99%

Influence of environmental factors on Olympic cross-country mountain bike performance

et al. 2020

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Olympic distance cross-country cycling (XCO) is a discipline subject to wide performance variability due to uncontrollable environmental factors such as altitude, ambient temperature and/or humidity. This study therefore aimed to investigate the impact of environmental factors on XCO performance in under-23 and elite female and male categories.Individual data were collected from Continental Cup, World Cup, World Championship, and Olympics Games for U23 and elite female and male categories from 2009 to 2018. Factors included were race time (range: 55-157 min), average speed (range: 7.6-32.2 km/h), distance (range: 15.2-48.4 km), altitude (range: 50-2680 m), ambient temperature (range 7-41°C), relative and absolute humidity (range: 8-97% and 2.4-25.3 g/m 3 , respectively), and categories.The analysis represents 10,966 individual data which indicate a continuous progression of the performance for all categories. Principal component analysis reveals that the slowest XCO performance was resulting from high ambient temperature and absolute humidity. Regressions revealed that only altitude (P < 0.0001) have a direct linear negative effect on XCO average speed. A significant negative interaction effect of altitude with absolute humidity (P < 0.0001) on XCO average speed was also found. In addition, the higher the absolute humidity, the higher is the impact of ambient temperature (P < 0.0001) on XCO average speed.While XCO performance progressed over time regardless of the categories, results also indicate that altitude, ambient temperature, and absolute humidity negatively impact XCO performance.

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“…These investigations henceforth indicated the importance of describing and understanding braking patterns during Olympic-format cross-country mountain bike (XCO-MTB) racing, and supported the idea that braking did indeed affect performance. These reports are important because they addressed questions concerning bicycle handling efficiency that had previously remained unquantified (Miller et al, 2017b;Miller et al, 2018b;Chidley et al, 2014;Hurst et al, 2006;Macdermid et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Calculation of rear brake power and rear brake work during skidding on paved and gravel cycling surfaces

Miller

Tully²,

Miller³

et al. 2019

J Sci Cycling

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The use of a brake power meter at each wheel of a bicycle is a valid means to calculate energy losses due to braking. However, methodology utilizing the torque and angular velocity at each wheel independently are not able to reflect energy lost to braking when the rear wheel is skidding. This study tested the possibility of using the angular velocity of the front wheel, but the torque of the rear brake, to calculate rear brake power. Two cyclists completed 100 braking trials across three days on a mixture of paved and gravel surfaces with a mixture of skidding and non-skidding. The estimated total energy removed from the bicycle-rider system was calculated as the sum of brake work and estimates of drag and rolling resistance. This energy removed from the bicycle-rider system displayed a strong positive relationship with the change in kinetic energy of the bicycle-rider system during braking on paved (r2=0.955; p<0.0001) and gravel surfaces paved (r2=0.702; p<0.0001). There was no difference between these measurements overall (p<0.05), however there is some error of measurement when skidding on gravel. The findings in the present investigation indicate that rear brake work is underestimated when using the angular velocity at the rear wheel during skidding, but that utilising the angular velocity of the front wheel is a valid means of calculating rear brake power. Care should be taken when skidding on gravel as it is difficult to assess the linear velocity of the bicycle.

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Performance and physiological effects of different descending strategies for cross‐country mountain biking

Cited by 14 publications

References 34 publications

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes

Influence of environmental factors on Olympic cross-country mountain bike performance

Calculation of rear brake power and rear brake work during skidding on paved and gravel cycling surfaces

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