The influence of pacing strategy on VO2 and supramaximal kayak performance

Physiological responses to 1000-m ergometer time-trial performance in outrigger canoeing

Graded exercise tests are commonly used to assess peak physiological capacities of athletes. However, unlike time trials, these tests do not provide performance information. The aim of this study was to examine the peak physiological responses of female outrigger canoeists to a 1000-m ergometer time trial and compare the time-trial performance to two graded exercise tests performed at increments of 7.5 W each minute and 15 W each two minutes respectively. 17 trained female outrigger canoeists completed the time trial on an outrigger canoe ergometer with heart rate (HR), stroke rate, power output, and oxygen consumption (VO2) determined every 15 s. The mean (+/- s) time-trial time was 359 +/- 33 s, with a mean power output of 65 +/- 16 W and mean stroke rate of 56 +/- 4 strokes min(-1). Mean values for peak VO2, peak heart rate, and mean heart rate were 3.17 +/- 0.67 litres min(-1), 177 +/- 11 beats min(-1), and 164 +/- 12 beats min(-1) respectively. Compared with the graded exercise tests, the time-trial elicited similar values for peak heart rate, peak power output, peak blood lactate concentration, and peak VO2. As a time trial is sport-specific and can simultaneously quantify sprint performance and peak physiological responses in outrigger canoeing, it is suggested that a time trial be used by coaches for crew selection as it doubles as a reliable performance measure and a protocol for monitoring peak aerobic capacity of female outrigger canoeists.

The Effect of Extrinsic Motivation on Cycle Time Trial Performance

PURPOSE

Athletes occasionally follow pacing patterns that seem unreasonably aggressive compared with those of prerace performances, potentially because of the motivation provided by competition. This study evaluated the effect of extrinsic motivation on cyclists' time trial performance.

METHODS

Well-trained recreational cyclists (N=7) completed four 1500-m laboratory time trials including a practice trial, two self-paced trials, and a trial where a monetary reward was offered. Time, total power output, power output attributable to aerobic and anaerobic metabolic sources, VO2, and HR were measured.

RESULTS

The time required for the second, third, and last (extrinsically motivated) time trials was 133.1 +/- 2.1, 134.1 +/- 3.4, and 133.6 +/- 3.0 s, respectively, and was not different (P>0.05). There were no differences for total (396 +/- 19, 397 +/- 23, and 401 +/- 17 W), aerobic (253 +/- 12, 254 +/- 10, and 246 +/- 13 W), and anaerobic (143 +/- 14, 143 +/- 21, and 155 +/- 11 W) power output. The highest VO2 was not different over consecutive time trials (3.76 +/- 0.19, 3.73 +/- 0.16, and 3.71 +/- 0.22 L x min(-1)). When ranked by performance, without reference to the extrinsic motivation (131.9 +/- 2.4, 133.4 +/- 2.4, and 135.4 +/- 2.5 s), there was a significant difference for the first 100 m and from 100 to 300 m in power output, with a larger total power (560 +/- 102, 491 +/- 82, and 493 +/- 93; and 571 +/- 94, 513 +/- 41, and 484 +/- 88 W) and power attributable to anaerobic sources (446 +/- 100, 384 +/- 80, and 324 +/- 43; and 381 +/- 87, 383 +/- 90, and 289 +/- 91 W) for the fastest trial.

CONCLUSION

Extrinsic motivation did not change the time trial performance, suggesting that 1500-m performance is extremely stable and not readily changeable with simple external motivation. The results suggest that spontaneous improvement in performance for time trials of this duration is attributable to greater early power output, which is primarily attributable to anaerobic metabolic sources.

Energy system contribution to 400-metre and 800-metre track running

As a wide range of values has been reported for the relative energetics of 400-m and 800-m track running events, this study aimed to quantify the respective aerobic and anaerobic energy contributions to these events during track running. Sixteen trained 400-m (11 males, 5 females) and 11 trained 800-m (9 males, 2 females) athletes participated in this study. The participants performed (on separate days) a laboratory graded exercsie test and multiple race time-trials. The relative energy system contribution was calculated by multiple methods based upon measures of race VO2, accumulated oxygen deficit (AOD), blood lactate and estimated phosphocreatine degradation (lactate/PCr). The aerobic/anaerobic energy system contribution (AOD method) to the 400-m event was calculated as 41/59% (male) and 45/55% (female). For the 800-m event, an increased aerobic involvement was noted with a 60/40% (male) and 70/30% (female) respective contribution. Significant (P < 0.05) negative correlations were noted between race performance and anaerobic energy system involvement (lactate/PCr) for the male 800-m and female 400-m events (r = - 0.77 and - 0.87 respectively). These track running data compare well with previous estimates of the relative energy system contributions to the 400-m and 800-m events. Additionally, the relative importance and speed of interaction of the respective metabolic pathways has implications to training for these events.

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.

Constant versus variable-intensity during cycling: effects on subsequent running performance

The aim of this study was to investigate the metabolic responses to variable versus constant-intensity (CI) during 20-km cycling on subsequent 5-km running performance. Ten triathletes, not only completed one incremental cycling test to determine maximal oxygen uptake and maximal aerobic power (MAP), but also three various cycle-run (C-R) combinations conducted in outdoor conditions. During the C-R sessions, subjects performed first a 20-km cycle-time trial with a freely chosen intensity (FCI, approximately 80% MAP) followed by a 5-km run performance. Subsequently, triathletes were required to perform in a random order, two C-R sessions including either a CI, corresponding to the mean power of FCI ride, or a variable-intensity (VI) during cycling with power changes ranging from 68 to 92% MAP, followed immediately by a 5-km run. Metabolic responses and performances were measured during the C-R sessions. Running performance was significantly improved after CI ride (1118 +/- 72 s) compared to those after FCI ride (1134 +/- 64 s) or VI ride (1168 +/- 73 s) despite similar metabolic responses and performances reported during the three cycling bouts. Moreover, metabolic variables were not significantly different between the run sessions in our triathletes. Given the lack of significant differences in metabolic responses between the C-R sessions, the improvement in running time after FCI and CI rides compared to VI ride suggests that other mechanisms, such as changes in neuromuscular activity of peripheral skeletal muscle or muscle fatigue, probably contribute to the influence of power output variation on subsequent running performance.

An Analysis of Pacing Strategies During Men’s World-Record Performances in Track Athletics

Purpose:

To analyze pacing strategies employed during men's world-record performances for 800-m, 5000-m, and 10,000-m races.

Methods:

In the 800-m event, lap times were analyzed for 26 world-record performances from 1912 to 1997. In the 5000-m and 10,000-m events, times for each kilometer were analyzed for 32 (1922 to 2004) and 34 (1921 to 2004) world records.

Results:

The second lap in the 800-m event was significantly slower than the first lap (52.0 ± 1.7 vs 54.4 ± 4.9 seconds, P < .00005). In only 2 world records was the second lap faster than the first lap. In the 5000-m and 10,000-m events, the first and final kilometers were significantly faster than the middle kilometer intervals, resulting in an overall even pace with an end spurt at the end.

Conclusion:

The optimal pacing strategy during world-record performances differs for the 800-m event compared with the 5000-m and 10,000-m events. In the 800-m event, greater running speeds are achieved in the first lap, and the ability to increase running speed on the second lap is limited. In the 5000-m and 10,000-m events, an end spurt occurs because of the maintenance of a reserve during the middle part of the race. In all events, pacing strategy is regulated in a complex system that balances the demand for optimal performance with the requirement to defend homeostasis during exercise.

The Role of Information Processing Between the Brain and Peripheral Physiological Systems in Pacing and Perception of Effort

This article examines how pacing strategies during exercise are controlled by information processing between the brain and peripheral physiological systems. It is suggested that, although several different pacing strategies can be used by athletes for events of different distance or duration, the underlying principle of how these different overall pacing strategies are controlled is similar. Perhaps the most important factor allowing the establishment of a pacing strategy is knowledge of the endpoint of a particular event. The brain centre controlling pace incorporates knowledge of the endpoint into an algorithm, together with memory of prior events of similar distance or duration, and knowledge of external (environmental) and internal (metabolic) conditions to set a particular optimal pacing strategy for a particular exercise bout. It is proposed that an internal clock, which appears to use scalar rather than absolute time scales, is used by the brain to generate knowledge of the duration or distance still to be covered, so that power output and metabolic rate can be altered appropriately throughout an event of a particular duration or distance. Although the initial pace is set at the beginning of an event in a feedforward manner, no event or internal physiological state will be identical to what has occurred previously. Therefore, continuous adjustments to the power output in the context of the overall pacing strategy occur throughout the exercise bout using feedback information from internal and external receptors. These continuous adjustments in power output require a specific length of time for afferent information to be assessed by the brain's pace control algorithm, and for efferent neural commands to be generated, and we suggest that it is this time lag that crates the fluctuations in power output that occur during an exercise bout. These non-monotonic changes in power output during exercise, associated with information processing between the brain and peripheral physiological systems, are crucial to maintain the overall pacing strategy chosen by the brain algorithm of each athlete at the start of the exercise bout.

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.

An analysis of the pacing strategy adopted by elite competitors in 2000 m rowing

OBJECTIVES

To determine the pacing strategies adopted by elite rowers in championship 2000 m races.

METHODS

Split times were obtained for each boat in every heavyweight race of the Olympic Games in 2000 and World Championships in 2001 and 2002, and the top 170 competitors in the British Indoor Rowing Championships in 2001 and 2002. Data were only included in subsequent analysis if there was good evidence that the athlete or crew completed the race in the fastest possible time. The remaining data were grouped to determine if there were different strategies adopted for on-water versus ergometer trials, "winners" versus "losers", and men versus women.

RESULTS

Of the 1612 on-water race profiles considered, 948 fitted the inclusion criteria. There were no differences in pacing profile between winners and losers, and men and women, although on-water and ergometry trials showed a competitively meaningful significant difference over the first 500 m sector. The average profile showed that rowers performed the first 500 m of the race faster than subsequent sectors-that is, at a speed of 103.3% of the average speed for the whole race, with subsequent sectors rowed at 99.0%, 98.3%, and 99.7% of average speed for on-water rowing, and 101.5%, 99.8%, 99.0%, and 99.7% for ergometry.

CONCLUSIONS

These data indicate that all athletes or crews adopted a similar fast start strategy regardless of finishing position or sex, although the exact pace profile was dependent on rowing mode. This strategy should be considered by participants in 2000 m rowing competitions.

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.

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.

Warm up I: potential mechanisms and the effects of passive warm up on exercise performance

Despite limited scientific evidence supporting their effectiveness, warm-up routines prior to exercise are a well-accepted practice. The majority of the effects of warm up have been attributed to temperature-related mechanisms (e.g. decreased stiffness, increased nerve-conduction rate, altered force-velocity relationship, increased anaerobic energy provision and increased thermoregulatory strain), although non-temperature-related mechanisms have also been proposed (e.g. effects of acidaemia, elevation of baseline oxygen consumption (.VO(2)) and increased postactivation potentiation). It has also been hypothesised that warm up may have a number of psychological effects (e.g. increased preparedness). Warm-up techniques can be broadly classified into two major categories: passive warm up or active warm up. Passive warm up involves raising muscle or core temperature by some external means, while active warm up utilises exercise. Passive heating allows one to obtain the increase in muscle or core temperature achieved by active warm up without depleting energy substrates. Passive warm up, although not practical for most athletes, also allows one to test the hypothesis that many of the performance changes associated with active warm up can be largely attributed to temperature-related mechanisms.

Development of race profiles for the performance of a simulated 2000-m rowing race

The purpose of this study was to determine the race profile for a 2000-m simulated rowing race as well as the effect of training and gender on the race profile. Nineteen men and 19 women undertook a 2000-m simulated rowing race before and after 10 weeks of a typical off-season training program for rowing. Velocity was calculated every 200 m and the deviation in velocities from the mean race velocity (MRV) was plotted every 200 m to produce race profiles for each gender before and after training. The three fastest male rowers varied approximately 0.02 m.s from the MRV after training and displayed a constant-pace model. The fastest female rowers displayed an all-out strategy after training, producing large deviations from MRV. Average squared deviations from the mean (SDM) determined that all groups except the fastest females had a reduction in MRV deviation after training. These results suggest that the optimal race profile for a simulated 2000-m rowing race may be different between genders. Training reduces SDM and influences both male and female pacing patterns such that both exhibit a pacing strategy that is more similar to that of elite athletes in other events of similar and shorter duration.

The influence of pacing during 6-minute supra-maximal cycle ergometer performance

The aim of this study was to evaluate the influence of pacing on performance, oxygen uptake (VO2), oxygen deficit and blood lactate accumulation during a 6-minute cycle ergometer test. Six recreational cyclists completed three 6-minute cycling tests using fast-start, even-pacing and slow-fast pacing conditions. Cycle ergometer performance was measured as the mean power output produced for each cycling test. Energy system contribution during each cycling trial was estimated using a modified accumulated oxygen deficit (AOD) method. Blood lactate concentration was analysed from blood sampled using a catheter in a forearm vein prior to exercise, at 2 minutes, 4 minutes and 6 minutes during exercise, and at 2 minutes, 5 minutes and 10 minutes post-exercise. There was no significant difference between the pacing conditions for mean power output (P = 0.09), peak VO2 (P = 0.92), total VO2 (P = 0.76), AOD (P = 0.91), the time-course of VO2 (P = 0.22) or blood lactate accumulation (P= 0.07). There was, however, a significant difference between the three pacing conditions in the oxygen deficit measured over time (P = 0.02). These changes in the time-course of oxygen deficit during cycling trials did not, however, significantly affect the mean power output produced by each pacing condition.

Pattern of energy expenditure during simulated competition

PURPOSE

To determine how athletes spontaneously use their energetic reserves when the only instruction was to finish in minimal time, and whether experience from repeated performance changes the strategy of recreational athletes.

METHODS

Recreational road cyclists/speed skaters (N = 9) completed three laboratory time trials of 1500 m on a windload braked cycle. The pattern of energy use was calculated from total work and from the work attributable to aerobic metabolism, which allowed computation of anaerobic energy use. Regional level speed skaters (N = 8) also performed a single 1500-m time trial with the same protocol and measurements.

RESULTS

The serial trials were completed in (mean +/- SD) 133.8 +/- 6.6, 133.9 +/- 5.8, 133.8 +/- 5.5 s (P > 0.05 among trials); and in 125.7 +/- 10.9 s in the skaters (P < 0.05 vs cyclists). The [OV0312]O(2peak) during the terminal 200 m was similar within trials (3.23 +/- 0.44, 3.34 +/- 0.44, 3.30 +/- 0.51 (P > 0.05)) versus 3.91 +/- 0.68 L.min-1 in the skaters (P < 0.05 vs cyclists). In all events, the initial power output and anaerobic energy use was high and decayed to a more or less constant value ( approximately 25% of peak) over the remainder of the event. Contrary to predictions based on an assumed "all out" starting strategy, the subjects reserved some of their ability to perform anaerobic work for a terminal acceleration. The total work accomplished was not different between trials (43.53, 43.78, and 47.48 kJ in the recreational athletes, or between the cyclists and skaters (47.79 kJ). The work attributable to anaerobic sources was not different between the rides (20.67, 20.53, and 21.12 kJ in the recreational athletes). In the skaters, the work attributable to anaerobic sources was significantly larger versus the cyclists (24.67 kJ).

CONCLUSION

Energy expenditure during high-intensity cycling seems: 1) to be expended in a manner that allows the athlete to preserve an anaerobic energetic contribution throughout an event, 2) does not appear to have a large learning effect in already well trained cyclists, and 3) anaerobic energy expenditure may be the performance discriminating factor among groups of athletes.