User:Jhopker

What is cycling efficiency?
Cycling efficiency describes the conversion of physical work into the mechanical work achieved on a bicycle. The ability to convert the available energy into mechanical work is an important consideration for cycling performance. Conceptually, two individuals could be performing at the same level of physical stress, but if one individual has a higher level of efficiency they can derive a higher power output for that energy cost. Cycling efficiency values from 16% to 24% have been reported in the scientific literature. Cycling efficiency, defined as the ratio of power output to energy expenditure, has been suggested to be a key determinant of endurance cycling performance During steady-state cycle ergometry, cycling efficiency has been extensively used to provide a convenient index of the effectiveness with which an individual can convert chemical energy into mechanical power output Several methods exist to calculate cycling efficiency, including net efficiency, work efficiency, delta efficiency, and gross efficiency. The calculation of net efficiency requires the energy expenditure at rest to be measured (and assigned to the performance of internal work). This is then subtracted from the total amount of energy expended for the external work accomplished. However, net efficiency does not account for the energy cost of moving the involved limbs. Gaesser and Brooks (1975) suggested that for a valid measurement this cost should be taken into account.

By measuring the energetic cost of cycling at a work rate of 0 W (i.e. unloaded pedaling) and subtracting this from the total energy expenditure for the measured work rate, the calculation of work efficiency accounts for the additional cost of moving the legs. In practice, the measurement of zero load in cycling can be problematic. Kautz and Neptune argued that zero load cycling does not provide a valid baseline that can be subtracted from a range of subsequent work intensities. This is due to the fact that the pattern of muscle recruitment during zero load cycling does not reflect that of loaded cycling.

Gaesser and Brooks (1975) proposed a floating baseline measure of the physiological and external energy cost of exercise, i.e. delta efficiency (DE). Here energy expenditure at a lower work rate is subtracted from the energy expenditure at a higher work rate ([delta work accomplished / delta energy expended] x 100). Coyle et al. suggested that this is the most valid calculation of whole-body efficiency as it attempts to partial out the influence of unmeasured work. However, the linear correlation that is produced between energy cost and work rate does not necessarily mean that it is a valid measure, i.e. that it is independent of the work rate used. For cycling efficiency to increase linearly with power output energy expenditure must increase non-linearly due to the decreasing relative contribution of non-propulsive factors (e.g. basal metabolism and leg movement). However, the use of DE may also be limited as it has been shown to have greater day-to-day variability than gross efficiency Due to the criticisms of base-line subtraction methods outlined above, it is unrealistic to attempt to attribute a portion of the total body energy cost to muscle work during a whole-body exercise such as cycling.

The most commonly used measure of cycling efficiency is gross efficiency ([work accomplished / energy expended] x 100). In calculating gross efficiency, the caloric equivalent of steady state O2 and the respiratory exchange ratio (RER) are used to calculate energy expenditure. ‘Gross efficiency’ is normally reported as a percentage of total energy expenditure. Readers are referred to a recent review where the concept of cycling efficiency has been extensively discussed.

What is the link between efficiency and performance?
Mathematical modeling has been used to predict that a 1% increase in a cyclist’s gross efficiency would result in a 63-second improvement in 40-km time-trial time. However, very few scientific studies have directly investigated the link between efficiency and cycling performance. Horowitz et al. divided an apparently homogeneous group of 14 endurance-trained cyclists according to gross efficiency during a 1-hour laboratory time-trial (i.e. a high- and a low-efficiency group). Both groups maintained the same level of oxygen consumption throughout the time-trial, but the high-efficiency group were able to generate 10% more power. In a reanalysis of cycling efficiency and cycling performance data from five separate studies, Jobson et al. showed that gross efficiency explained 34% and 26% of the variation in power output during long and short cycling time-trials respectively. Whilst other variables, notably VO2max and lactate threshold, have been shown to explain more of the variance in cycling performance, this study confirmed the important influence of cycling efficiency on performance.

The effects of training
Gross efficiency during cycling has been reported to be in the range of 16-24%. An improvement in efficiency implies an increase in mechanical power output for a given metabolic cost. Using the extremes of the 16-24% range suggests that for the same rate of metabolic energy expenditure, an efficient cyclist would produce 28% more power than a less efficient cyclist ((23 – 18)/18 * 100 = 28%).

Given that cycling efficiency effects cycling performance, if efficiency could be increased, performance would be improved. Several cross-sectional studies have failed to find any differences in efficiency between trained and untrained cyclists. However, a number of these investigations can be criticised on the basis of their methods and their failure to address the risk of committing a type 2 statistical error. Specifically, a lack of statistical power in many of these research studies resulted in an inability to detect significant differences between study populations. Other confounding factors include the usage of short stage protocols and artificially imposed cadences.

Evidence of longitudinal changes in gross efficiency was provided by Coyle who reported an 8.8% increase in the gross efficiency of a Grand Tour Champion over a 7-year period. Unfortunately, it was not possible to tightly control all aspects of data collection in this case study. Consequently, it has been the subject of repeated criticism for its design, method and analysis. Sassi et al. have reported (non statistically significant) seasonal changes in gross efficiency in a small group of competitive cyclists. Using a longitudinal study design, Hopker et al. demonstrated that competitive cyclists can increase their gross efficiency by as much as 5%. These increases were significantly correlated with the volume and intensity of training completed. More recently, Hopker et al. reported increases in cycling efficiency following a period of 6 weeks of high-intensity cycling training. Though researchers have demonstrated that gross efficiency can increase as a result of training, it is still unclear whether or not such chronic changes actually impact on performance. Further research is required in this area.

How to measure cycling efficiency
To measure oxygen uptake and energy expenditure, to enable the calculation of cycling efficiency it is recommended that Douglas bags be used. Cyclists should initially complete a 10-minute warm up at a low intensity (e.g. 100W) using their preferred cadence. Following a short rest, cyclists should then complete 3-4 successive work rates (e.g. 150W, 180W, 210W, 240W). Either a preferred or fixed cadence can be used, but this should be kept constant throughout the test, and on any subsequent tests that are completed. Each power output should be maintained for at least 5-6 minutes to ensure stabilization of both oxygen consumption and carbon dioxide production. Expired air is then collected in Douglas bags during the last minute of each work rate (e.g. between the 5th and 6th minute of a 6-minute stage). It is recommended that the cyclist have a 5-minute rest between successive power outputs to enable time for “wash-out” of the effects of the power output from the preceding stage.

Due to the effect that changes in body position have on cycling efficiency it is important that trained cyclists use either their own bike during the test or have their bike set-up replicated on a laboratory ergometer. Untrained individuals should be allowed to adjust the bike to their own preference. These settings should then be replicated during any subsequent tests.

Following analysis of expired gases, energy expenditure can be determined from the energetic equivalent of VO2 using the table of non-protein respiratory quotient (Péronnet & Massicotte, 1991) and the VO2. To ensure steady-state oxygen consumption has been attained, the ratio between oxygen consumption and carbon dioxide production (respiratory exchange ratio or RER) should be <1.0. Cycling efficiency is then calculated as the ratio between work done and energy expended by using one of the following equations:

Gross Efficiency = (work completed / energy expended)*100%

Delta Efficiency = (delta work completed / delta energy expended)*100%

High ambient temperatures (35.5°C) have been shown to cause decreases in gross efficiency. With hyperthermia, the energy cost of the exercise may increase due to greater circulation, sweating and ventilation. This in turn may reduce cycling efficiency, as the work accomplished would remain unchanged. Therefore, ambient temperature should be tightly controlled, especially when repeating tests during different phases of a season under varied climatic conditions. Finally, the control of the cyclists’ pre-test regimen presents a further methodological issue. Passfield and Doust demonstrate that there is an acute reduction in gross efficiency following a period of 60 minutes cycling at 60% VO2max. This reduction in efficiency was significantly correlated with a lower 5-min time-trial performance. Similarly, there is also some evidence to suggest that muscle damage from high intensity training might decrease efficiency during subsequent exercise performance. Therefore, it is important to monitor training in the days prior to testing cycling efficiency. The time course for restoration of gross efficiency after exercise has not been established.

These methodological points are discussed further in a recent review article by Hopker et al.