Modeling road-cycling performance

Aerobic capacity and respiratory indices of junior cross-country skiers and biathletes during incremental exercise testing

The present study compared the isocapnic buffering phase (ICB), hypocapnic hyperventilation phase, ventilatory threshold (VT), respiratory compensation point (RCP), and maximum oxygen uptake (VO2max) among biathlon and cross-country ski athletes during an incremental exercise test. 37 male and 33 female Turkish National Team athletes volunteered to participate in the research. Body fat percentage, lean mass, and fat mass values of athletes were measured using the bioelectrical impedance analysis method, and oxygen consumption (VO2) was measured with a portable cardiopulmonary exercise test system with a ramp protocol on the treadmill. In VT, RCP, and VO2max phases, male athletes had higher VO2 and speed values than female athletes (p < 0.05). In contrast, they had similar values across different categories of sports (biathlon and cross-country skiing) (p > 0.05). Additionally, XC skiers and males had higher absolute (Abs) VO2 and mass-normalized (Rel) VO2 values than biathletes and females in exhaustion times and ICBs (p < 0.05). In contrast, they had similar Abs VO2 and Rel VO2 values in hypocapnic hyperventilation phases (p > 0.05). In addition, XC skiers and males had higher absolute (Abs) VO2 and relative (Rel) VO2 values than biathletes and females in exhaustion times and ICBs (p < 0.05). In contrast, they had similar Abs VO2 and Rel VO2 values in hypocapnic hyperventilation phases (p > 0.05). These results indicate significant differences in physiological profiles between male and female athletes and between XC skiers and biathletes.

Modelling road cycling as motion on a curve

We present a mathematical model of road cycling on arbitrary routes using the Frenet–Serret frame. The route is embedded in the coupled governing equations. We describe the mathematical model and numerical implementation. The dynamics are governed by a balance of forces of gravity, drag, and friction, along with pedalling or braking. We analyse steady-state speed and power against gradient and curvature. The centripetal acceleration is used as a control to determine transitions between pedalling and braking. In our model, the rider looks ahead at the curvature of the road by a distance dependent on the current speed. We determine such a distance (1–3 s at current speed) for safe riding and compare with the mean power. The results are based on a number of routes including flat and downhill, with variations in maximum curvature, and differing number of bends. We find the braking required to minimise centripetal acceleration occurs before the point of maximum curvature, thereby allowing acceleration by pedalling out of a bend.

Result-based talent identification in road cycling: discovering the next Eddy Merckx

In various sports large amounts of data are nowadays collected and analyzed to help scouts with identifying talented young athletes. In contrast, the literature on result-based talent identification in road cycling is remarkably scarce. The purpose of this paper is to provide insight into the possibilities of the use of publicly available data to discover new talented Under-23 (U23) riders via statistical learning methods (linear regression and random forest techniques). At the same time, we try to find out the main determinants of success for U23 riders in their first years of professional cycling. We collect results for more than 25000 road cycling races from 2007-2018 and consider more than 2500 riders from over 80 countries. We use the data from 2007 to 2017 to train and validate our models, and use the data from 2018 to predict how well U23 riders will perform in their first three elite years. Our results reveal that past U23 race results appear to be important predictors of future cycling performance.

Neiva H, Barbosa TM, Marinho DA. The Drag Crisis Phenomenon on an Elite Road Cyclist-A Preliminary Numerical Simulations Analysis in the Aero Position at Different Speeds

The drag crisis phenomenon is the drop of drag coefficient (C_d) with increasing Reynolds number (Re) or speed. The aim of this study was to assess the hypothetical drag crisis phenomenon in a sports setting, assessing it in a bicycle–cyclist system. A male elite-level cyclist was recruited for this research and his competition bicycle, helmet, suit, and shoes were used. A three-dimensional (3D) geometry was obtained with a 3D scan with the subject in a static aero position. A domain with 7 m of length, 2.5 m of width and 2.5 m of height was created around the cyclist. The domain was meshed with 42 million elements. Numerical simulations by computer fluid dynamics (CFD) fluent numerical code were conducted at speeds between 1 m/s and 22 m/s, with increments of 1 m/s. The drag coefficient ranged between 0.60 and 0.95 across different speeds and Re. The highest value was observed at 2 m/s (C_d = 0.95) and Re of 3.21 × 10⁵, whereas the lower C_d was noted at 9 m/s (C_d = 0.60) and 9.63 × 10⁵. A drag crisis was noted between 3 m/s and 9 m/s. Pressure C_d ranged from 0.35 to 0.52 and the lowest value was observed at 3 m/s and the highest at 2 m/s. The viscous drag coefficient ranged between 0.15 and 0.43 and presented a trend decreasing from 4 m/s to 22 m/s. Coaches, cyclists, researchers, and support staff must consider that C_d varies with speed and Re, and the bicycle–cyclist dimensions, shape, or form may affect drag and performance estimations. As a conclusion, this preliminary work noted a drag crisis between 3 m/s and 9 m/s in a cyclist in the aero position.

Body composition of female road and track endurance cyclists: Normative values and typical changes

The aims of this study were to describe normative values and seasonal variation of body composition in female cyclists comparing female road and track endurance cyclists, and to validate the use of anthropometry to monitor lean mass changes. Anthropometric profiles (seven site skinfolds) were measured over 16 years from 126 female cyclists. Lean mass index (LMI) was calculated as body weight × skinfolds(-x). The exponent (x) was calculated as the slope of the natural logarithm of body weight and skinfolds. Percentage changes in LMI were compared to lean mass changes measured using dual-energy X-ray absorptiometry (DXA) in a subset of 25 road cyclists. Compared to sub-elite and elite cyclists, world class cyclists were (mean [95% CI]) 1.18 kg [0.46, 1.90] and 0.60 kg [0.05, 1.15] lighter and had skinfolds that were 7.4 mm [3.8, 11.0] and 4.6 mm [1.8, 7.4] lower, respectively. Body weight (0.41 kg [0.04, 0.77]) and skinfolds (4.0 mm [2.1, 6.0]) were higher in the off-season compared to the early-season. World class female road cyclists had lower body weight (6.04 kg [2.73, 9.35]) and skinfolds (11.5 mm [1.1, 21.9]) than track endurance cyclists. LMI (mean exponent 0.15 [0.13, 0.18]) explained 87% of the variance in DXA lean mass. In conclusion, higher performing female cyclists were lighter and leaner than their less successful peers, road cyclists were lighter and leaner than track endurance cyclists, and weight and skinfolds were lowest early in the season. LMI appears to be a reasonably valid tool for monitoring lean mass changes.

Field-measured drag area is a key correlate of level cycling time trial performance

Drag area (Ad ) is a primary factor determining aerodynamic resistance during level cycling and is therefore a key determinant of level time trial performance. However, Ad has traditionally been difficult to measure. Our purpose was to determine the value of adding field-measured Ad as a correlate of level cycling time trial performance. In the field, 19 male cyclists performed a level (22.1 km) time trial. Separately, field-determined Ad and rolling resistance were calculated for subjects along with projected frontal area assessed directly (AP ) and indirectly (Est AP ). Also, a graded exercise test was performed to determine [Formula: see text] peak, lactate threshold (LT), and economy. [Formula: see text] peak ([Formula: see text]) and power at LT were significantly correlated to power measured during the time trial (r = 0.83 and 0.69, respectively) but were not significantly correlated to performance time (r = - 0.42 and -0.45). The correlation with performance time improved significantly (p < 0.05) when these variables were normalized to Ad . Of note, Ad alone was better correlated to performance time (r = 0.85, p < 0.001) than any combination of non-normalized physiological measure. The best correlate with performance time was field-measured power output during the time trial normalized to Ad (r = - 0.92). AP only accounted for 54% of the variability in Ad . Accordingly, the correlation to performance time was significantly lower using power normalized to AP (r = - 0.75) or Est AP (r = - 0.71). In conclusion, unless normalized to Ad , level time trial performance in the field was not highly correlated to common laboratory measures. Furthermore, our field-measured Ad is easy to determine and was the single best predictor of level time trial performance.

Fundamental Features of 16–18 Years Old Road Cyclists’ Training

Lithuanian young cyclists are the winners of various international road cycling events, however, methodic of their preparation, change of their body development, as well as of body and functional capacity indices during yearly training cycle have been of little investigation yet.  To have as many as possible objective criteria, helping to ensure a more fluent optimization of road cyclists’ training process and observation of change in their preparedness, it is relevant to carry out investigation on basic features of preparedness and training sessions, so that later on this data would be of use in improving preparation of young road cyclists. The aim of the work was to carry out analysis on young road cyclists’ preparation, change in their body and functional capacities in yearly training cycle, and to provide the summarized results. The results of the research highlighted good preparation of young Lithuanian cyclists in organizational aspect. Training load in yearly cycle reached 1 213 hours, work on road compiled 1006 hours, work on special cycle ergo meter – 118 hours, and 156 hours were allocated for general preparation. In one year, young athletes participated in 437 training sessions, and during them they overcame 22 115 km by bicycle. Muscle power indices of the investigated when performing short duration work did not experience change during the period of investigation. This typical for road cyclists feature was evidenced, confirming the fact that such physical ability is not characteristic to road cyclists. Young cyclists’ indices of special working capacity and indices of blood respiratory system functional capacity used to increase significantly in yearly training cycle. The cyclists, who participated in our research, were successful at Lithuanian and international competitions. The athletes’ demonstrated results testimony the appropriately balanced program of their training, and the progress of the athletes’ body and functional power during yearly training cycle. This allows presuming that these athletes have favorable perspectives to perfect their mastership in future.

Differences in physiological responses between short- vs. long-graded laboratory tests in road cyclists

This study aimed to determine the effect of a short-graded with respect to a long-graded protocol laboratory test on the physiological responses of road cyclists. Twenty well-trained road cyclists performed a short-graded and long-graded laboratory tests within 1 week of each other in a randomized and crossover study design. Blood lactate concentration ([La-]b), heart rate (HR), oxygen consumption ((Equation is included in full-text article.)), and carbon dioxide production ((Equation is included in full-text article.)) were measured. Fat and carbohydrate oxidation rates (FAT(OxR) and CHO(OxR)) were estimated at the end of each stage during the short-graded and the long-graded (10th minute: T2.10) and in the middle of long-graded (fifth minute: T2.5) protocol. Lactate threshold (LT) and individual anaerobic threshold (IAT) were calculated. For maximal intensities, duration and maxFAT(OxR) were significantly higher in long-graded with respect to short-graded protocols. Peak power output (POPeak), HRPeak, [La-]bmax, (Equation is included in full-text article.), and maxCHO(OxR) were significantly higher in short-graded with respect to long-graded protocols. At submaximal intensities, short-graded protocol provoked higher demands on glycolytic metabolism than long-graded protocol; no differences were illustrated for HR or (Equation is included in full-text article.)between protocols. Crossover concept shifted to higher intensities in long-graded with respect to short-graded protocols due to the higher lipolytic response during the long-graded protocol. Both LT and IAT were reached at the same %(Equation is included in full-text article.), although significantly higher PO in short-graded with respect to long-graded protocols was reached. The long-graded proved to be more specific than the short-graded protocol to assess the physiological responses of road cyclists based on relative PO (W·kg(-1)).

Accuracy of Indirect Estimation of Power Output From Uphill Performance in Cycling

Purpose:

To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest.

Methods:

Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3–6.3 km, slope 4.4–10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France.

Results:

Overall, the difference between Pmes and Pest was –0.95% (95%CI: –10.4%, +8.5%) for all trials and 0.24% (–6.1%, +6.6%) in conditions without wind (>2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial.

Conclusions:

Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

Energy expenditure of constant- and variable-intensity cycling: power meter estimates

PURPOSE

The objective of this study is to compare the effects of constant- and variable-intensity cycling on gross efficiency (GE) and to compare estimates of energy expenditure (EE) made using indirect calorimetry (CAL) with estimates derived from commercially available power meters.

METHODS

Nine national team female road cyclists completed a GE test (GEtest = 4 min at approximately 45%, approximately 55%, approximately 65%, and approximately 75% maximal aerobic power (MAP)) before and after 10.5 min of either constant- (CON)- or variable- (VAR)-intensity cycling averaging approximately 55% MAP. GE measured before, after, and during CON and VAR cycling was compared. Total EE (kJ) for 10.5 min of VAR cycling was estimated using indirect CAL and compared with estimates on the basis of mechanical power [Schoberer Rad Messtechnik (SRM)] using the group mean GE, each athlete's mean GE, and each athlete's power to GE regression.

RESULTS

There was no effect of VAR on GEtests (P = 0.74). GE reduced from 19.1% ± 0.4% (mean ± SE) during the pretrial GEtests to 18.7% ± 0.4% during the posttrial GEtests (P < 0.05) in both conditions. Differences in GE (mean ± SD) measured during CON (18.4% ± 1.6%) and VAR cycling (18.6% ± 1.1%) were trivial (P = 0.28). SRM-based estimates of EE were most accurate when using individual athlete's power GE regression using Pre- and Post-VAR GEtest data combined (Δ(Equation is included in full-text article.)(%) ± 90% CI, 0.3 ± 0.8; R 0.98, P <0.001). Group mean estimates were within approximately 1% of CAL, although individual errors were approximately 11%.

CONCLUSION

Findings support the use of calibrated power meters for estimating cycling EE. For trained female road cyclists, total mechanical work (kJ) multiplied by 5.3 (GE = 19%) provides a valid estimation of total EE during variable-intensity cycling <75% MAP, although determining each athlete's GE improves accuracy greatly.

Modelling the determinants of 2000 m rowing ergometer performance: a proportional, curvilinear allometric approach

Previous studies have investigated the determinants of indoor rowing using correlations and linear regression. However, the power demands of ergometer rowing are proportional to the cube of the flywheel's (and boat's) speed. A rower's speed, therefore, should be proportional to the cube root (0.33) of power expended. Hence, the purpose of the present study was to explore the relationship between 2000 m indoor rowing speed and various measures of power of 76 elite rowers using proportional, curvilinear allometric models. The best single predictor of 2000 m rowing ergometer performance was power at VO(2max)(WVO(2max))(0.28), that explained R(2)=95.3% in rowing speed. The model realistically describes the greater increment in power required to improve a rower's performance by the same amount at higher speeds compared with that at slower speeds. Furthermore, the fitted exponent, 0.28 (95% confidence interval 0.226-0.334) encompasses 0.33, supporting the assumption that rowing speed is proportional to the cube root of power expended. Despite an R(2)=95.3%, the initial model was unable to explain "sex" and "weight-class" differences in rowing performances. By incorporating anaerobic as well as aerobic determinants, the resulting curvilinear allometric model was common to all rowers, irrespective of sex and weight class.

Physiological and anthropometric characteristics of junior cyclists of different specialties and performance levels

This study analyzes the anthropometric and physiological characteristics of junior cyclists within different cycling specialties and different performance levels. One hundred and thirty-two junior riders (16.8 ± 0.6 years, 177 ± 6 cm, 66.3 ± 6.7 kg) were tested for anthropometric, aerobic and anaerobic parameters. Cyclists were classified within specialties [uphill (UH) flat terrain (FT) all terrain (AT) and sprint (SP)] and performance levels, based on a seasonal ranking [low level (LL) medium level (ML) and high level (HL)]. The results of the two-way analysis of variance showed that FT and SP have greater body dimensions than UH and AT (P<0.001). Concerning the relative aerobic parameters, AT and UH have higher values (P<0.001) than FT and SP [maximal oxygen uptake (VO(2max) ): 69.4 ± 3.6, 67.5 ± 5.0, 62.8 ± 4.5 and 61.9 ± 4.1 mL/kg/min, respectively] while absolute parameters resulted higher for FT and AT (P≤0.008). The relative power produced in the 5 s test was higher (P<0.001) for AT and SP than FT and UH (16.7 ± 1.1, 16.6 ± 0.6, 14.9 ± 1.7 and 14.4 ± 1.7 W/kg, respectively). Concerning the performance level, only the age and the aerobic parameters resulted differently within levels (VO(2max) : HL=67.3 ± 4.9, ML=65.5 ± 5.1 and LL=63.3 ± 5.2 mL/kg/min), with the highest values for HL (P≤0.007). In conclusion, juniors are specialized in the same way as professional cyclists and the aerobic characteristics are confirmed as significant in the performance level assessment.

The energetics of cycling on Earth, Moon and Mars

From 1885, technological improvements, such as the use of special metal alloys and the application of aerodynamics principles, have transformed the bicycle from a human powered heavy transport system to an efficient, often expensive, object used to move not only in our crowded cities, but also in leisure activities and in sports. In this paper, the concepts of mechanical work and efficiency of cycling together with the corresponding metabolic expenditure are discussed. The effects of altitude and aerodynamic improvements on sports performances are also analysed. A section is dedicated to the analysis of the maximal cycling performances. Finally, since during the next decades the return of Man on the Moon and, why not, a mission to Mars can be realistically hypothesised, a section is dedicated to cycling-based facilities, such as man powered short radius centrifuges, to be used to prevent cardiovascular and skeletal muscle deconditioning otherwise occurring during long-term exposure to microgravity.

The analysis and utilization of cycling training data

Most mathematical models of athletic training require the quantification of training intensity and quantity or 'dose'. We aim to summarize both the methods available for such quantification, particularly in relation to cycle sport, and the mathematical techniques that may be used to model the relationship between training and performance. Endurance athletes have used training volume (kilometres per week and/or hours per week) as an index of training dose with some success. However, such methods usually fail to accommodate the potentially important influence of training intensity. The scientific literature has provided some support for alternative methods such as the session rating of perceived exertion, which provides a subjective quantification of the intensity of exercise; and the heart rate-derived training impulse (TRIMP) method, which quantifies the training stimulus as a composite of external loading and physiological response, multiplying the training load (stress) by the training intensity (strain). Other methods described in the scientific literature include 'ordinal categorization' and a heart rate-based excess post-exercise oxygen consumption method. In cycle sport, mobile cycle ergometers (e.g. SRM and PowerTap) are now widely available. These devices allow the continuous measurement of the cyclists' work rate (power output) when riding their own bicycles during training and competition. However, the inherent variability in power output when cycling poses several challenges in attempting to evaluate the exact nature of a session. Such variability means that average power output is incommensurate with the cyclist's physiological strain. A useful alternative may be the use of an exponentially weighted averaging process to represent the data as a 'normalized power'. Several research groups have applied systems theory to analyse the responses to physical training. Impulse-response models aim to relate training loads to performance, taking into account the dynamic and temporal characteristics of training and, therefore, the effects of load sequences over time. Despite the successes of this approach it has some significant limitations, e.g. an excessive number of performance tests to determine model parameters. Non-linear artificial neural networks may provide a more accurate description of the complex non-linear biological adaptation process. However, such models may also be constrained by the large number of datasets required to 'train' the model. A number of alternative mathematical approaches such as the Performance-Potential-Metamodel (PerPot), mixed linear modelling, cluster analysis and chaos theory display conceptual richness. However, much further research is required before such approaches can be considered as viable alternatives to traditional impulse-response models. Some of these methods may not provide useful information about the relationship between training and performance. However, they may help describe the complex physiological training response phenomenon.

Understanding sprint-cycling performance: the integration of muscle power, resistance, and modeling

Sprint-cycling performance is paramount to competitive success in over half the world-championship and Olympic races in the sport of cycling. This review examines the current knowledge behind the interaction of propulsive and resistive forces that determine sprint performance. Because of recent innovation in field power-measuring devices, actual data from both elite track- and road-cycling sprint performances provide additional insight into key performance determinants and allow for the construction of complex models of sprint-cycling performance suitable for forward integration. Modeling of various strategic scenarios using a variety of field and laboratory data can highlight the relative value for certain tactically driven choices during competition.

Effects of gradient and speed on freely chosen cadence: the key role of crank inertial load

The purpose of this study was to describe the relationship between road gradient (RG) and freely chosen cadence (FCC) in a group of professional cyclists during their normal training. In addition, a calculation of crank inertial load (CIL) was estimated in order to establish the relationship between FCC and CIL. Ten professional cyclists were monitored during training using commercially available power meters (Shoberer Rad Messtechnik (SRM), professional version). For each cyclist, recorded training sessions were reviewed to identify the hardest 6-8 training sessions (approximately 18 h of training). RG was estimated based on the relationship between power output, total mass and speed. The analysis was performed using 2113+/-317 samples of 30 s average data, collected on terrain ranging from -4%RG to 12%RG. The individual relationship between FCC and RG could be described by a linear regression model. There was a moderate correlation between FCC and CIL (group's r=0.42), and a multiple regression including the measured power output (WPO) increased the variance explained (R2=0.24). The correlation was very large between CIL and v (r=0.91), and was not strengthened by adding WPO as an independent variable (r=0.91). In conclusion, this investigation documents that in professional cyclists engaged in training, there is a linear decrease in FCC as RG increases (-4%RG and 12%RG). This decrease in FCC appears to be due to the reduction in v as slope increases. It is surmised that CIL plays a key role in the modulation of FCC.

Comparison of different theoretical models estimating peak power output and maximal oxygen uptake in trained and elite triathletes and endurance cyclists in the velodrome

The aim of this study was to assess which of the equations that estimate peak power output and maximal oxygen uptake (VO2max) in the velodrome adapt best to the measurements made by reference systems. Thirty-four endurance cyclists and triathletes performed one incremental test in the laboratory and two tests in the velodrome. Maximal oxygen uptake and peak power output were measured with an indirect calorimetry system in the laboratory and with the SRM training system in the velodrome. The peak power output and VO2max of the field test were estimated by means of different equations. The agreement between the estimated and the reference values was assessed with the Bland-Altman method. The equation of Olds et al. (1995) showed the best agreement with respect to the peak power output reference values, and that of McCole et al. (1990) was the only equation to show good agreement with respect to the VO2max reference values. The VO2max values showed a higher coefficient of determination with respect to maximal aerobic speed when they were expressed in relative terms. In conclusion, the equations of Olds et al. (1995) and McCole et al. (1990) were best at estimating peak power output and VO2max in the velodrome, respectively.

The effects of exercise intensity or drafting during swimming on subsequent cycling performance in triathletes

The purpose of this study was to compare the affects of drafting or a reduction of exercise intensity during swimming on the power output sustained (P(mean)) during a subsequent cycle time trial (TT). In addition the relationship between peak power output (PPO) and P(mean) generated during the cycle TT after swimming was examined. Nine well-trained triathletes performed an incremental cycling test to exhaustion for determination of PPO. In addition, each subject performed three swim-cycle (SC) trials consisting of 20 min cycle TT preceded by a 400 m swimming trial completed as (1) "all out" and in a non-drafting situation (SC(100%)); (2) at 90% of SC(100%) in a non-drafting situation (SC(90%)); (3) in a drafting position at the same controlled velocity as SC(100%) (SC(drafting)). Swimming velocity (ms(-1)) was significantly (p<0.01) lower at each time point during the 400 m swimming trial in SC(90%) compared with SC(100%) and SC(drafting). There was no significant difference in velocity between SC(100%) and SC(drafting). Blood lactate (BLA) concentration was also significantly (p<0.01) lower after swimming in SC(90%) compared to SC(100%) and SC(drafting) (3.8+/-0.9 versus 7.3+/-2.4 and 7.9+/-2.4mM). The Pmean was also significantly (p<0.05) lower in SC(100%) relative to the SC(90%) and SC(drafting) (226+/-15 versus 253+/-33 and 249+/-36W). There was no significant correlation between PPO (W) and P(mean) for SC(100%) (r=-0.32), SC(90%) (r=0.65; p=0.058) or SC(drafting) (r=0.54). This study indicates that drafting or swimming at a lower velocity did not induce any conflicting affects on power output during a subsequent cycle TT. However, this study confirms that P(mean) during a cycle TT is reduced when prior swimming is performed. Furthermore the positive relationship typically observed between PPO and P(mean) is disrupted by swimming activity performed before a cycling TT. This factor should be considered in terms of physiological analysis of triathletes.

Pacing during an elite Olympic distance triathlon: comparison between male and female competitors

This study investigated whether pacing differed between 68 male and 35 female triathletes competing over the same ITU World Cup course. Swimming, cycling and running velocities (m s(-1) and km h(-1)) were measured using a global positioning system (Garmin, UK), video analysis (Panasonic NV-MX300EG), and timing system (Datasport, Switzerland). The relationship between performance in each discipline and finishing position was determined. Speed over the first 222 m of the swim was associated with position (r=-0.88 in males, r=-0.97 in females, both p<0.01) and offset from the leader, at the swim finish (r=-0.42 in males, r=-0.49 in females, both p<0.01). The latter affected which pack number was attained in bike lap 1 (r=0.81 in males, r=0.93 in females, both p<0.01), bike finishing position (both r=0.41, p<0.01) and overall finishing position (r=0.39 in males, r=0.47 in females, both p<0.01). Average biking speed, and both speed and pack attained in bike laps 1 and 2, influenced finishing position less in the males (r=-0.42, -0.2 and -0.42, respectively, versus r=-0.74, -0.75, and -0.72, respectively, in the females, all p<0.01). Average run speed correlated better with finishing position in males (r=-0.94, p<0.01) than females (r=-0.71, p<0.001). Both sexes ran faster over the first 993 m than most other run sections but no clear benefit of this strategy was apparent. The extent to which the results reflect sex differences in field size and relative ability in each discipline remains unclear.

Comparison of nine theoretical models for estimating the mechanical power output in cycling

OBJECTIVE

To assess which of the equations used to estimate mechanical power output for a wide aerobic range of exercise intensities gives the closest value to that measured with the SRM training system.

METHODS

Thirty four triathletes and endurance cyclists of both sexes (mean (SD) age 24 (5) years, height 176.3 (6.6) cm, weight 69.4 (7.6) kg and Vo(2)max 61.5 (5.9) ml/kg/min) performed three incremental tests, one in the laboratory and two in the velodrome. The mean mechanical power output measured with the SRM training system in the velodrome tests corresponding to each stage of the tests was compared with the values theoretically estimated using the nine most referenced equations in literature (Whitt (Ergonomics 1971;14:419-24); Di Prampero et al (J Appl Physiol 1979;47:201-6); Whitt and Wilson (Bicycling science. Cambridge: MIT Press, 1982); Kyle (Racing with the sun. Philadelphia: Society of Automotive Engineers, 1991:43-50); Menard (First International Congress on Science and Cycling Skills, Malaga, 1992); Olds et al (J Appl Physiol 1995;78:1596-611; J Appl Physiol 1993;75:730-7); Broker (USOC Sport Science and Technology Report 1-24, 1994); Candau et al (Med Sci Sports Exerc 1999;31:1441-7)). This comparison was made using the mean squared error of prediction, the systematic error and the random error.

RESULTS

The equations of Candau et al, Di Prampero et al, Olds et al (J Appl Physiol 1993;75:730-7) and Whitt gave a moderate mean squared error of prediction (12.7%, 21.6%, 13.2% and 16.5%, respectively) and a low random error (0.5%, 0.6%, 0.7% and 0.8%, respectively).

CONCLUSIONS

The equations of Candau et al and Di Prampero et al give the best estimate of mechanical power output when compared with measurements obtained with the SRM training system.

Aerodynamic Characteristics as Determinants of the Drafting Effect in Cycling

PURPOSE

To determine whether cyclists' individual aerodynamic characteristics influence the magnitude of the drafting effect in cycling.

METHODS

Thirteen competitive male cyclists performed two field protocols (individual and drafting). Hub-based power meters were used to measure power output and velocity, from which drag area (Ad) was calculated. The three subjects obtaining maximum (MAX), intermediate (INT), and minimum (MIN) values for Ad during the individual protocol acted as leaders during the drafting protocol. Measures of Ad were then made while subjects drafted each of the three leaders. The drafting effect was specifically quantified as the decrement on measured drag coefficient (Cd) and power output.

RESULTS

The mean drafting effect increased with leader Ad (DeltaCd: MIN = 35.55%, INT = 41.31%, MAX = 50.47%; Delta power: MIN = 111.1 W, INT = 124.05 W, MAX = 159.23 W; P < 0.0001). Regressions between leader Ad and drafting effect for individual drafters indicated substantial interdrafter variability (slope ranged from 29.4 to 190.5%.m) but little intradrafter variability (mean r = 0.9689), suggesting an interaction between leader and drafter. Correlating leader:drafter ratios for Ad, Ap, and body mass to the drafting effect supported this interaction (r = 0.69-0.78, P < 0.01), but only when data for all three groups were pooled.

CONCLUSIONS

Alteration of leader Ad elicits a linear increase in the drafting effect. However, the Ad of the leader does not explain all of the interdrafter variability in the drafting effect, which is specific to the drafting subject but is minimally explained by their aerodynamic characteristics. This interdrafter variability may be attributable to the drafter's skill in obtaining maximum benefit from drafting.

Distribution of Power Output During Cycling

We aim to summarise the impact and mechanisms of work-rate pacing during individual cycling time trials (TTs). Unlike time-to-exhaustion tests, a TT provides an externally valid model for examining how an initial work rate is chosen and maintained by an athlete during self-selected exercise. The selection and distribution of work rate is one of many factors that influence cycling speed. Mathematical models are available to predict the impact of factors such as gradient and wind velocity on cycling speed, but only a few researchers have examined the inter-relationships between these factors and work-rate distribution within a TT. When environmental conditions are relatively stable (e.g. in a velodrome) and the TT is >10 minutes, then an even distribution of work rate is optimal. For a shorter TT (< or = 10 minutes), work rate should be increased during the starting effort because this proportion of total race time is significant. For a very short TT (< or = 2 minutes), the starting effort should be maximal, since the time saved during the starting phase is predicted to outweigh any time lost during the final metres because of fatigue. A similar 'time saving' rationale underpins the advice that work rate should vary in parallel with any changes in gradient or wind speed during a road TT. Increasing work rate in headwind and uphill sections, and vice versa, decreases the variability in speed and, therefore, the total race time. It seems that even experienced cyclists naturally select a supraoptimal work rate at the start of a longer TT. Whether such a start can be 'blunted' through coaching or the monitoring of psychophysiological variables is unknown. Similarly, the extent to which cyclists can vary and monitor work rate during a TT is unclear. There is evidence that sub-elite cyclists can vary work rate by +/-5% the average for a TT lasting 25-60 minutes, but such variability might be difficult with high-performance cyclists whose average work rate during a TT is already extremely high (>350 watts). During a TT, pacing strategy is regulated in a complex anticipatory system that monitors afferent feedback from various physiological systems, and then regulates the work rate so that potentially limiting changes do not occur before the endpoint of exercise is reached. It is critical that the endpoint of exercise is known by the cyclist so that adjustments to exercise work rate can be made within the context of an estimated finish time. Pacing strategies are thus the consequence of complex regulation and serve a dual role: they are both the result of homeostatic regulation by the brain, as well as being the means by which such regulation is achieved. The pacing strategy 'algorithm' is sited in the brain and would need afferent input from interoceptors, such as heart rate and respiratory rate, as well as exteroceptors providing information on local environmental conditions. Such inputs have been shown to induce activity in the thalamus, hypothalamus and the parietal somatosensory cortex. Knowledge of time, modulated by the cerebellum, basal ganglia and primary somatosensory cortex, would also input to the pacing algorithm as would information stored in memory about previous similar exercise bouts. How all this information is assimilated by the different regions of the brain is not known at present.

Validation of a field test to determine the maximal aerobic power in triathletes and endurance cyclists

OBJECTIVE

To validate a field test to assess the maximal and submaximal exercise aerobic adaptation under specific conditions, for endurance modality cyclists and triathletes.

METHODS

30 male and 4 female endurance modality cyclists and triathletes, with heterogeneous performance levels, performed three incremental tests: one in the laboratory and two in the field. Assessment of the validity of the field protocol was carried out by the Student's t test, intraclass correlation coefficient (ICC) and coefficient of variation (CV) of the maximal variables (maximal aerobic speed (MAS), maximal aerobic power (MAP), maximal heart rate (HR(max)), maximal blood lactate concentration ([La(-)](max)) and maximal oxygen uptake (VO(2max))) and submaximal variables (heart rate, HR) measured in each one of the tests. The errors in measurement were calculated. The repeatability of the field tests was assessed by means of the test-retest of the two field tests, and the validity by means of the test-retest of the laboratory test with respect to the mean of the two field tests.

RESULTS

No significant differences were found between the two field tests for any of the variables studied, but differences did exist for some variables between the laboratory tests with respect to the field tests (MAP, [La(-)](max), humidity (H), barometric pressure (Pb) and some characteristics of the protocols). The ICC of all the variables was high and the CV for the MAP was small. Furthermore, the measurement errors were small and therefore, assumable.

CONCLUSIONS

The incremental protocol of the proposed field test turned out to be valid to assess the maximal and submaximal aerobic adaptation.

Modeling sprint cycling using field-derived parameters and forward integration

We previously reported that a mathematical model could accurately predict steady-state road-cycling power when all the model parameters were known. Application of that model to competitive cycling has been limited by the need to obtain accurate parameter values, the non-steady-state nature of many cycling events, and because the validity of the model at maximal power has not been established.

PURPOSE

We determined whether modeling parameters could be accurately determined during field trials and whether the model could accurately predict cycling speed during maximal acceleration using forward integration.

METHODS

First, we quantified aerodynamic drag area of six cyclists using both wind tunnel and field trials allowing for these two techniques to be compared. Next, we determined the aerodynamic drag area of three world-class sprint cyclists using the field-test protocol. Track cyclists also performed maximal standing-start time trials, during which we recorded power and speed. Finally, we used forward integration to predict cycling speed from power-time data recorded during the maximal trials allowing us to compare predicted speed with measured speed.

RESULTS

Field-based values of aerodynamic drag area (0.258 +/- 0.006 m) did not differ (P = 0.53) from those measured in a wind tunnel (0.261 +/- 0.006 m2). Forward integration modeling accurately predicted cycling speed (y = x, r2 = 0.989) over the duration of the standing-start sprints.

CONCLUSIONS

Field-derived values for aerodynamic drag area can be equivalent to values derived from wind tunnel testing, and these values can be used to accurately predict speed even during maximal-power acceleration by world-class sprint cyclists. This model could be useful for assessing aerodynamic issues and for predicting how subtle changes in riding position, mass, or power output will influence cycling speed.

Optimal power-to-mass ratios when predicting flat and hill-climbing time-trial cycling

The purpose of this article was to establish whether previously reported oxygen-to-mass ratios, used to predict flat and hill-climbing cycling performance, extend to similar power-to-mass ratios incorporating other, often quick and convenient measures of power output recorded in the laboratory [maximum aerobic power (W(MAP)), power output at ventilatory threshold (W(VT)) and average power output (W(AVG)) maintained during a 1 h performance test]. A proportional allometric model was used to predict the optimal power-to-mass ratios associated with cycling speeds during flat and hill-climbing cycling. The optimal models predicting flat time-trial cycling speeds were found to be (W(MAP)m(-0.48))(0.54), (W(VT)m(-0.48))(0.46) and (W(AVG)m(-0.34))(0.58) that explained 69.3, 59.1 and 96.3% of the variance in cycling speeds, respectively. Cross-validation results suggest that, in conjunction with body mass, W(MAP) can provide an accurate and independent prediction of time-trial cycling, explaining 94.6% of the variance in cycling speeds with the standard deviation about the regression line, s=0.686 km h(-1). Based on these models, there is evidence to support that previously reported VO2-to-mass ratios associated with flat cycling speed extend to other laboratory-recorded measures of power output (i.e. Wm(-0.32)). However, the power-function exponents (0.54, 0.46 and 0.58) would appear to conflict with the assumption that the cyclists' speeds should be proportional to the cube root (0.33) of power demand/expended, a finding that could be explained by other confounding variables such as bicycle geometry, tractional resistance and/or the presence of a tailwind. The models predicting 6 and 12% hill-climbing cycling speeds were found to be proportional to (W(MAP)m(-0.91))(0.66), revealing a mass exponent, 0.91, that also supports previous research.

The critical power and related whole-body bioenergetic models

This paper takes a performance-based approach to review the broad expanse of literature relating to whole-body models of human bioenergetics. It begins with an examination of the critical power model and its assumptions. Although remarkably robust, this model has a number of shortcomings. Attention to these has led to the development of more realistic and more detailed derivatives of the critical power model. The mathematical solutions to and associated behaviour of these models when subjected to imposed "exercise" can be applied as a means of gaining a deeper understanding of the bioenergetics of human exercise performance.

Scaling maximal oxygen uptake to predict cycling time-trial performance in the field: a non-linear approach

The purpose of the present article is to identify the most appropriate method of scaling VO2max for differences in body mass when assessing the energy cost of time-trial cycling. The data from three time-trial cycling studies were analysed (N = 79) using a proportional power-function ANCOVA model. The maximum oxygen uptake-to-mass ratio found to predict cycling speed was VO2max(m)(-0.32) precisely the same as that derived by Swain for sub-maximal cycling speeds (10, 15 and 20 mph). The analysis was also able to confirm a proportional curvilinear association between cycling speed and energy cost, given by (VO2max(m)(-0.32))0.41. The model predicts, for example, that for a male cyclist (72 kg) to increase his average speed from 30 km h(-1) to 35 km h(-1), he would require an increase in VO2max from 2.36 l min(-1) to 3.44 l min(-1), an increase of 1.08 l min(-1). In contrast, for the cyclist to increase his mean speed from 40 km h(-1) to 45 km h(-1), he would require a greater increase in VO2max from 4.77 l min(-1) to 6.36 l min(-1), i.e. an increase of 1.59 l min(-1). The model is also able to accommodate other determinants of time-trial cycling, e.g. the benefit of cycling with a side wind (5% faster) compared with facing a predominately head/tail wind (P<0.05). Future research could explore whether the same scaling approach could be applied to, for example, alternative measures of recording power output to improve the prediction of time-trial cycling performance.

Experimental evaluation of the power balance model of speed skating

Prediction of speed skating performance with a power balance model requires assumptions about the kinetics of energy production, skating efficiency, and skating technique. The purpose of this study was to evaluate these parameters during competitive imitations for the purpose of improving model predictions. Elite speed skaters (n = 8) performed races and submaximal efficiency tests. External power output (P(o)) was calculated from movement analysis and aerodynamic models and ice friction measurements. Aerobic kinetics was calculated from breath-by-breath oxygen uptake (Vo(2)). Aerobic power (P(aer)) was calculated from measured skating efficiency. Anaerobic power (P(an)) kinetics was determined by subtracting P(aer) from P(o). We found gross skating efficiency to be 15.8% (1.8%). In the 1,500-m event, the kinetics of P(an) was characterized by a first-order system as P(an) = 88 + 556e(-0.0494t) (in W, where t is time). The rate constant for the increase in P(aer) was -0.153 s(-1), the time delay was 8.7 s, and the peak P(aer) was 234 W; P(aer) was equal to 234[1 - e(-0.153(t-8.7))] (in W). Skating position changed with preextension knee angle increasing and trunk angle decreasing throughout the event. We concluded the pattern of P(aer) to be quite similar to that reported during other competitive imitations, with the exception that the increase in P(aer) was more rapid. The pattern of P(an) does not appear to fit an "all-out" pattern, with near zero values during the last portion of the event, as assumed in our previous model (De Koning JJ, de Groot G, and van Ingen Schenau GJ. J Biomech 25: 573-580, 1992). Skating position changed in ways different from those assumed in our previous model. In addition to allowing improved predictions, the results demonstrate the importance of observations in unique subjects to the process of model construction.

The Isocapnic Buffering Phase and Mechanical Efficiency: Relationship to Cycle Time Trial Performance of Short and Long Duration

The purpose of this study was to determine the relationship between the isocapnic buffer (βisocapnic) and hypocapnic hyperventilation (HHV) phases as well as performance in a short (20-min) and long (90-min) time trial (TT) in trained athletes. In addition, gross (GE, %) and delta (ΔE, %) efficiency were calculated and the relationship between these variables and the average power output (W) in each TT was determined. Thirteen male endurance athletes (Mean ± SD age 31 ± 6 yrs; body mass 75.6 ± 6.3 kg; height 185 ± 6 cm) completed a continuous incremental test to exhaustion for determination of the βisocapnic and HHV phases. A second submaximal test was used to determine GE and ΔE. The average power output (W) was measured in a 20-min and 90-min cycling TT. The βisocapnic phase (W) was significantly correlated to the average power output (W) in the 20-min TT (r = 0.58; p <  0.05), but not in the 90-min TT (r = 0.28). The HHV phase (W) was not significantly correlated to the average power output in the 20-min or 90-min TT. No significant correlation was found for GE or for ΔE and performance in the TT. The data from this study shows that βisocapnic together with HHV is not likely to be a useful indicator of cycle TT performance of 20- to 90-min duration. Furthermore, GE and ΔE determined from a submaximal incremental stepwise test are not related to cycling TT performance of different duration. Key words: incremental, correlation, metabolism, athletes, fatigue

The Science of Cycling

This review presents information that is useful to athletes, coaches and exercise scientists in the adoption of exercise protocols, prescription of training regimens and creation of research designs. Part 2 focuses on the factors that affect cycling performance. Among those factors, aerodynamic resistance is the major resistance force the racing cyclist must overcome. This challenge can be dealt with through equipment technological modifications and body position configuration adjustments. To successfully achieve efficient transfer of power from the body to the drive train of the bicycle the major concern is bicycle configuration and cycling body position. Peak power output appears to be highly correlated with cycling success. Likewise, gear ratio and pedalling cadence directly influence cycling economy/efficiency. Knowledge of muscle recruitment throughout the crank cycle has important implications for training and body position adjustments while climbing. A review of pacing models suggests that while there appears to be some evidence in favour of one technique over another, there remains the need for further field research to validate the findings. Nevertheless, performance modelling has important implications for the establishment of performance standards and consequent recommendations for training.

Body size as a determinant of the 1-h cycling record at sea level and altitude

This study was designed to validate models for predicting the two Union Cycliste Internationale (UCI) classifications for the 1-h cycling record at sea level and altitude. Specific attention was paid to the integration of model components that were sensitive to scaling differences in body mass (m(b)). The Modern Aero Position model predicted UCI Best Hour performances using predictions of total projected frontal area (A(P)) that included use of an aerodynamic bicycle and aerodynamic handlebars. The Traditional Racing Position model predicted UCI Hour Record performances using predictions of total A(P) that include use of a "Merckx-era" bicycle with drop-style handlebars. Prediction equations for A(P), as well as the coefficient of drag and metabolic power output , involved scaling relationships with m(b), while other model components were similar to previously published 1-h models. Both models were solved for the distance cycled in 1 h (D(HR)) using an iterative strategy. Chris Boardman's current records for the UCI Best Hour Performance (56.375 km) and the UCI Hour Record (49.202 km) were underpredicted by only 0.332 km (-0.6%) and 0.239 km (-0.5%). Both models, regardless of altitude, suggested that D(HR) should scale with m(b) to the +0.174 power (D(HR) alpha m(b) (+0.174)), which is lower than the +0.32 exponent recently suggested in the literature. Lastly, the same models also predicted that six-time Tour de France winner, Lance Armstrong, could exceed both of Boardman's current records at sea level by about 2.0 km.

Patterns of active transport in 11-12 year old Australian children

OBJECTIVES

To describe the habitual transport patterns of 11 to 12-year-old children in Australia, to determine the personal and environmental factors associated with active transport (AT), and to quantify how much AT contributes to overall daily energy expenditure (EE).

METHODS

The participants in this study were 136 children aged 11-12 year olds from eight randomly chosen primary schools in Adelaide, South Australia. Each child recalled their trips on two school days and a non-school day. Mass and stature were measured, and children completed a computerised activity recall and a neighbourhood satisfaction questionnaire. Trips were categorised according to their destination, child and parent dissatisfaction with the neighbourhood, and the gender, socio-economic status (SES), BMI and activity levels of the children undertaking them. These categories, along with the distance to the destination, were used as independent variables in a logistic regression model, with trip mode (passive versus active) as the dependent variable.

RESULTS

Children made an average of 1.0 active trips per day, with a median trip length of 0.63 km, while the median total distance covered actively per child per day was 0.61 km. Twenty-six per cent of children did no AT over the three days, and 67% did no AT on a weekend day. Distance was by far the strongest predictor of the likelihood that a trip would be active. Trips made by girls were less likely to be active compared with boys. Trips to the shops were less likely to be active than trips to school. Children's AT accounted for 1.3% of their daily EE.

CONCLUSIONS AND IMPLICATIONS

The active transport levels of children were very low. Interventions should focus on making neighbourhoods safer and more accessible to children and should promote bicycle use.

Science and cycling: current knowledge and future directions for research

In this holistic review of cycling science, the objectives are: (1) to identify the various human and environmental factors that influence cycling power output and velocity; (2) to discuss, with the aid of a schematic model, the often complex interrelationships between these factors; and (3) to suggest future directions for research to help clarify how cycling performance can be optimized, given different race disciplines, environments and riders. Most successful cyclists, irrespective of the race discipline, have a high maximal aerobic power output measured from an incremental test, and an ability to work at relatively high power outputs for long periods. The relationship between these characteristics and inherent physiological factors such as muscle capilliarization and muscle fibre type is complicated by inter-individual differences in selecting cadence for different race conditions. More research is needed on high-class professional riders, since they probably represent the pinnacle of natural selection for, and physiological adaptation to, endurance exercise. Recent advances in mathematical modelling and bicycle-mounted strain gauges, which can measure power directly in races, are starting to help unravel the interrelationships between the various resistive forces on the bicycle (e.g. air and rolling resistance, gravity). Interventions on rider position to optimize aerodynamics should also consider the impact on power output of the rider. All-terrain bicycle (ATB) racing is a neglected discipline in terms of the characterization of power outputs in race conditions and the modelling of the effects of the different design of bicycle frame and components on the magnitude of resistive forces. A direct application of mathematical models of cycling velocity has been in identifying optimal pacing strategies for different race conditions. Such data should, nevertheless, be considered alongside physiological optimization of power output in a race. An even distribution of power output is both physiologically and biophysically optimal for longer ( > 4 km) time-trials held in conditions of unvarying wind and gradient. For shorter races (e.g. a 1 km time-trial), an 'all out' effort from the start is advised to 'save' time during the initial phase that contributes most to total race time and to optimize the contribution of kinetic energy to race velocity. From a biophysical standpoint, the optimum pacing strategy for road time-trials may involve increasing power in headwinds and uphill sections and decreasing power in tailwinds and when travelling downhill. More research, using models and direct power measurement, is needed to elucidate fully how much such a pacing strategy might save time in a real race and how much a variable power output can be tolerated by a rider. The cyclist's diet is a multifactorial issue in itself and many researchers have tried to examine aspects of cycling nutrition (e.g. timing, amount, composition) in isolation. Only recently have researchers attempted to analyse interrelationships between dietary factors (e.g. the link between pre-race and in-race dietary effects on performance). The thermal environment is a mediating factor in choice of diet, since there may be competing interests of replacing lost fluid and depleted glycogen during and after a race. Given the prevalence of stage racing in professional cycling, more research into the influence of nutrition on repeated bouts of exercise performance and training is required.

Interpreting skinfold sums. Use of absolute or relative typical error?

The main aims of this study were to: 1) quantify how divergent the sum of seven skinfolds (Sigma7(skinfolds)) of athletes have to be before a general index of measurement imprecision (typical error, TE) is no longer appropriate to very lean or somewhat overweight athletes, and 2) discuss the application of absolute or relative (TE%) typical errors. The Sigma7(skinfolds) was measured in duplicate on 101 athletes by one level 3 anthropometrist with one calibrated skinfold caliper. The TE and TE% for Sigma7(skinfolds) were calculated for all data (TE(ALL), TE%(ALL)), as well as for results less than 50 mm (TE(<50), TE%(<50)), inclusive of 50-74.9 mm (TE(50-74.9), TE%(50-74.9)), inclusive of 75-99.9 mm (TE(75-99.9), TE%(75-99.9)) and 100 mm and greater (TE(>or=100), TE%(>or=100)). At least 20 samples were taken for each measurement range. Limits of agreement (LoA) at the 68% and 95% confidence level were also calculated for all data and the four skinfold ranges. The TE, TE 68, and 95% LoA increased as a direct function of the total Sigma7(skinfolds) and the general TE(ALL) was inappropriate for TE(<50) and TE(>or=100). In contrast, the %TE was very similar within each confidence level (ranging from 1.6-2.1% and 3.1-4.2% for the 68% and 95% LoA, respectively) regardless of the skinfold total. To minimise inaccurate feedback to individuals, anthropometrists dealing with skinfolds of elite athletes should establish an absolute TE on a homogeneous sample of athletes with a Sigma7(skinfolds) in a narrow band, for instance <50 mm. We also urge prudence in interpreting change in skinfold totals and suggest that the 95% level of confidence is appropriate in most instances.

Drafting distance in swimming

PURPOSE

This study investigates the effect of the distance separating the lead and draft swimmers on the metabolic and hydrodynamic responses of the draft swimmer.

METHODS

A nondrafting swim of 4 min at 95% of the best 1500-m pace for 11 swimmers was compared with swimming in a drafting position at four different distances directly behind another swimmer (0, 50, 100, and 150 cm). Swimming performance was assessed by stroke rate and stroke length; the metabolic response by oxygen uptake, heart rate, and blood lactate; and the rating of perceived exertion by the Borg scale. Passive drag was assessed at these drafting distances by passive towing. Then, passive drag was measured in six swimmers towed in six lateral drafting positions, with swimmers separated by approximately 40 cm, and then measured in two positions at the rear of the lead swimmer with a reduced lateral distance between swimmers of 50 and 0 cm.

RESULTS

Oxygen uptake, heart rate, blood lactate, rating of perceived exertion, and stroke rate were significantly reduced and stroke length was significantly increased in all drafting positions compared with the nondrafting position. For drag, the most advantageous drafting distances were 0 and 50 cm back from the toes of the lead swimmer. Drag was reduced by 21% and 20%, respectively. In lateral drafting, drag was significantly reduced by 6% and 7%, respectively, at 50 and 100 cm back from the hands of the lead swimmer.

CONCLUSIONS

Swimming behind another swimmer at a distance between 0 and 50 cm back from the toes was the most advantageous, whereas in lateral drafting the optimal distance was 50-100 cm back from the hands of the lead swimmer.

Intensity of exercise according to topography in professional cyclists

PURPOSE

The aim of the study was to analyze the intensity of effort made by professional cyclists in the different mountain passes climbed during the 1999 and 2000 Vuelta a España.

METHODS

During the ascent of high mountain passes of different categories (special category (HMS), and 1st (HM1), 2nd (HM2), and 3rd category (HM3)), the response of the HR was analyzed according to three intensity zones: zone 1(Z1, above the ventilatory threshold (VT)), zone 2 (Z2, between VT and the respiratory compensation threshold (RCT)), and zone 3 (Z3, above the RCT).

RESULTS

The values are presented as mean +/- SEM. Values of HR were significantly higher (P < 0.05) in HM1 (160 +/- 1 beats x min-1) compared with the other types of ascents. When we compared the different passes, the intensity decreased in the following order: HM1, HMS, HM2, and HM3. The average time that cyclists spent in Z3 was significantly higher in HM1 (10.7 +/- 1.4 min) with respect to the other categories. The time in Z2 was significantly higher in HMS and HM1 (43.1 +/- 1.5 and 44.3 +/- 3.1 min) than in HM2 and HM3 (21.6 +/- 1.1 and 11.9 +/- 1.1 min). The percentage of total time spent in Z3 was significantly higher in HM1 and HM3 (21.2 +/- 2.9 and 17.3 +/- 1.9%) than in HME and HM2.

CONCLUSION

The ascent of mountain passes is an activity involving intense effort which is reflected in the time cyclists spend in Z3 and Z2, and is related to the category of the mountain passes involved.

Modelling human locomotion: applications to cycling

Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a 'level playing field' to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables.