Article Text
Abstract
Purpose Athletes are trained to choose the pace which is perceived to be correct during a specific effort, such as the 1500m speed skating competition. The purpose of the present study was to “override” selfpaced (SP) performance by instructing athletes to execute a theoretically optimal pacing profile.
Methods Seven nationallevel speedskaters performed a SP 1500m which was analysed by obtaining velocity (every 100 m) and body position (every 200 m) with video to calculate total mechanical power output. Together with gross efficiency and aerobic kinetics, obtained in separate trials, data were used to calculate aerobic and anaerobic power output profiles. An energy flow model was applied to SP, simulating a range of pacing strategies, and a theoretically optimal pacing profile was imposed in a second race (IM).
Results Final time for IM was ∼2 s slower than SP. Total power distribution per lap differed, with a higher power over the first 300 m for IM (637.0 (49.4) vs 612.5 (50.0) W). Anaerobic parameters did not differ. The faster first lap resulted in a higher aerodynamic drag coefficient and perhaps a less effective pushoff.
Conclusion Experienced athletes have a welldeveloped performance template, and changing pacing strategy towards a theoretically optimal fast start protocol had negative consequences on speedskating technique and did not result in better performance.
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Athletes seem to choose their pacing strategy from the starting point of exercise, adjusted to their physiological condition.1 Apparently, the tradeoff between benefits of extra power early in the race versus the liability of earlier fatigue is solved by the athlete by performing a virtual calculation. Homeostatic disturbances are balanced against the goal of competition, a process of trial and error. Continuous adjustments in power output within an event are apparently associated with information processing between the brain and peripheral physiological systems,2 3 reflected in the rating of perceived exertion (RPE). Even when incorrect distance feedback is provided, RPE patterns do not change over the course of a selfpaced (SP) 20km race.4 Athletes follow a predetermined exercise template with a growth in RPE proportional to the event distance,5 6 which is important in preventing unacceptably large disturbances in homeostasis7 8 and maximising performance. Developing and using this template has been referred to as teleoanticipation,9 an internal negotiation regarding estimation of task remaining, momentary power output, estimated energetic reserves10 11 and alreadyperformed exercise.12
Though several studies focussed on pacing strategies in endurance performance, the present study evaluates anaerobic energy distribution during middle distance performance, where anaerobic energy represents a quantitatively large percentage of total energy requirement. Performance can be modelled using an energy flow model.13,–,18 Different pacing strategies can be simulated to determine the value of different strategies1 14 19 predicting that middle distance athletic events (<2 min) can be improved with a relatively fast initial pace.1 14 18 Although experienced athletes are apparently well equipped to determine pacing strategy, they do not always follow the theoretical optimal pacing profile. In the present study, the performance template of SP 1500m speed skating was “overridden” by imposing a pacing strategy based on a theoretically optimal profile.1 14 18 This allowed us to test the hypothesis emerging from modelling studies that a faster initial pace would result in better performance.
Methods
Participants
Seven nationallevel male speed skaters provided written informed consent and participated in the study. The protocol was approved by the University of Calgary Conjoint Health Research Ethics Board. Mean participant height was 181 (6) cm, mean body mass was 77 (5) kg and mean age was 20 (3) years.
Protocol
Four skating trials were performed at an indoor 400m skating oval at 1035 m above sea level (Calgary, Canada). On separate days, athletes performed two competitive 1500m races: one SP and one with an imposed pacing strategy (IM) based on calculations from the energy flow model. To obtain the individual characteristics necessary as input for the energy flow model, gross efficiency (GE) was determined during submaximal skating by measuring oxygen consumption (V̇O_{2}) and mechanical power output. V̇O_{2} response was measured in a preliminary 1500m trial. Both trials preceded SP and IM. Before every trial, barometric pressure was measured. Mean ice temperature was measured at eight locations on the track, and ice friction coefficient was estimated.20 To obtain knee and trunk angles for determination of mechanical power output,14 digital video recordings were made on the straight segments of the track. Timing was done by digital cameras at the entry and exit of each corner, and average velocity and power output of each segment were calculated.15 Because of the large acceleration in the initial 100 m, velocity at the first measurement camera (at 100 m) was assumed to be 1.3 times the mean velocity over the first 100 m, based on secondbysecond highspeed recorded velocity profiles obtained at the 1500m Salt Lake City Olympics (unpublished).
Submaximal trial
Athletes skated nine laps at a submaximal intensity intended to be sustainable for 30–60 min. V̇O_{2} was measured using a portable metabolic system (Cosmed K4b2, Rome, Italy). The gas analyser was calibrated with room air and reference gas (15.19% O_{2}, 6.06% CO_{2}). Volume measurement was calibrated using a 3l syringe. Skating velocity, knee and trunk angles were obtained to calculate mechanical power output. GE was determined15 based on calculations of mechanical power output and V̇O_{2} with the respiratory exchange ratio (RER)21: metabolic power attributable to aerobic sources (P_{met}) was calculated from the V̇O_{2} and V̇CO_{2} data as follows:
where P_{met} is the aerobic metabolic power output in W, V̇O_{2} is the oxygen consumption in l• min^{−1} and RER is the respiratory exchange ratio. Constants are based on the energy equivalence of V̇O_{2}, taking into consideration the mixture of substrate metabolised. An RER in excess of 1.0 was attributable to buffering; thus, RER >1.0 was treated as if equal to 1.0 relative to calculating metabolic work. GE was calculated as follows:
where P_{tot} is the mechanical power produced by the skater.14
V̇O_{2} response
To describe the mechanical power achieved by aerobic energy (P_{aer}) over time, necessary for modelling performance, V̇O_{2} data were collected in a 1500m trial preceding the experimental trials and interpolated to 1s values. Participants were instructed to finish as fast as possible as in competition. The V̇O_{2} response was fit by a least squares monoexponential model22 that has previously been applied to timetrial exercise.14,–,16 23 The cardiodynamic phase was omitted from the fitting fields based on visual inspection. By multiplying the V̇O_{2} response by the energy equivalent of oxygen (20.98 kJ/l O_{2}, since RER >1) and GE, P_{aer} was calculated, resulting in a rate constant (λ) and a maximal aerobic power output (P_{aermax}; equation 1). Equation 1 is presented without the time delay that was taken up into fitting the V̇O_{2} response, because for modelling performance, it will not be used.
(1)Experimental trials
The first race was SP and part of a national competition. SP was used to obtain the remaining model parameters (ie, the description for calculated P_{an} over time) necessary to simulate different pacing strategies. Based on the prediction of the energy flow model, the coaches' advice and 300m sprint times, skaters were instructed to start IM at a relatively fast pace outside their normal pacing range, conforming to the results of De Koning et al14 and Hettinga et al23 This strategy was closer to the theoretically optimal strategy but still within the range of commonsense possibilities, particularly considering that the start of 1500 m is very close to their top skating velocity for 300 m already. To get acquainted with the “feel” of the faster start, the skaters practiced the faster start in two training sessions, completing 3 × 300 and 2 × 700 m at maximal velocity.
Velocity and power output were compared per lap, and anaerobic power profile parameters were compared. To represent differences in pacing strategy, correcting for possible differences in external conditions, velocity was normalised to mean velocity. To identify the effect of a changing skating posture during the race, air friction coefficient (k_{air})24 was compared.
The energy flow model
By using the energy flow model including expressions for mechanical power produced by the speed skater (P_{tot}) and mechanical power used to overcome frictional forces (P_{friction}), SP and IM were modelled, and different pacing profiles were simulated.14,–,18 The interplay between P_{tot} and P_{friction} results in temporal changes in kinetic energy (dE_{kin}/dt) of the skater (equation 2), leading to a certain velocity profile over the race.
(2)P_{friction} can be described by the summation of power used to overcome air frictional forces (P_{air}, equation 3) and ice resistance (P_{ice}, equation 4).
(3)The air friction coefficient (k_{air}) is affected by postural changes over the race and is calculated according to van Ingen Schenau (26) using the measured trunk and knee angles; v equals velocity in a no wind situation, as in the present (indoor) study. P_{air} is adjusted for barometric pressure. P_{ice} was calculated from the skater's body weight (m · g), coefficient of ice friction (µ) and v25 (equation 4); µ was estimated using onice temperature.20
(4)P_{tot} equals the sum of mechanical power production by the aerobic (P_{aer}) and anaerobic (P_{an}) energy system (equation 5).
(5)Both P_{aer} and P_{an} can be described over time, as already described for P_{aer} (equation 1). P_{aer} was determined during a supramaximal preceding effort. The timecourse of this energy delivery was assumed to be identical in each trial. P_{an} was calculated by subtracting P_{aer} from P_{tot}. This difference was fit to equation 6.
(6)P_{an} decreases monoexponentially from a peak (P_{anpeak}) at the beginning of the race to a constant level described by P_{ancon}, the asymptote of equation 6. Peak anaerobic power (P_{anpeak}) is the sum of P_{ancon} and P_{anmax}, which is more relevant to performance than either P_{ancon} or P_{anmax} separately. The anaerobic rate constant γ14 23 dictates the rate of decline from P_{anpeak} to P_{ancon}. For comparing individual datum, the instantaneous value of P_{an} at the end of exercise is taken.
Optimal pacing strategy
Because pacing strategy in the supramaximal domain is mainly determined by the distribution of anaerobic energy,19 the effect of manipulating anaerobic parameters P_{ancon}, P_{anmax} and γ was studied by simulating different performances. The parameters of P_{an} were varied with the restriction that total anaerobic work generated over the race, and all other variables, were kept constant. The combination of P_{anmax}, γ and P_{ancon} leading to the fastest time corresponds to optimal pacing strategy.
Statistics
SP and IM were compared per lap with ANOVA repeated measures (strategy × lap). The parameters of the anaerobic power profiles were compared with a paired sample t test (p<0.05).
Results
Submaximal trial
Mean GE was 18.7% (1.0%), skated at a mean power output of 185.7 (16.0) W, with a mean velocity of 9.59 (0.40) m/s and a mean RER of 0.98 (0.04). Mean knee angle was 110.7º (9.8º), and mean trunk angle was 18.5º (6.0º).
V̇O_{2} response
The mean amplitude of the V̇O_{2} response, that is, the highest V̇O_{2} observed during the pretrial, was 3.3 (0.3l)·min^{−1}, with a mechanical equivalent P_{aermax} of 218.7 (20.6) W. Mean λ (equation 1) was 0.125 (0.37) s. Times were relatively slow (121.99 (2.94) s). A typical example of V̇O_{2} response of a representative participant is shown (fig 1).
SP trial versus imposed strategy trial
Estimated icefriction coefficient was slightly higher in IM (0.0047 (0.0000)), where ice temperature was −4.5 ºC (0.0ºC) compared to SP (0.0044 (0.0002)), where ice temperature was −6.2ºC (1.1ºC). Barometric pressure was also slightly higher in the IM (897.9 (1.2) hP) compared to SP (896.6 (0.4) hP).
Final times of SP (115.39 (4.45) s) were significantly faster than IM (117.29 (3.53) s). Lap times and velocity profiles are shown (table 1). Main effects for absolute (v_{abs}) and relative velocity normalised to mean velocity (v_{rel}) were significantly different between pacing strategies. Values for the aerodynamic drag coefficient (k_{air}; table 2) were higher throughout the entire race for IM. P_{tot} was significantly greater during the opening 300 m of IM and decreased significantly per lap in both SP and IM (table 3). An interaction (strategy × lap) effect was found for the distribution of P_{tot} per lap, demonstrating that the values for P_{tot} varied by lap, and this change was dependent on which trial was considered. Essentially, this means the change in P_{tot} through the race was different between trials, resulting in different (relative) velocity profiles over the race.
Energy flow model prediction of the theoretically optimal pacing strategy
Optimal pacing strategy was predicted using GE, the response V̇O_{2} and the velocity profile from the SP trial. This trial allowed calculation of the P_{an}. For all participants, the theoretically optimal performance could be achieved by skating a faster initial pace, conforming to previous data.1 14 18 Less mechanical energy, which is related to the value of P_{ancon}, must be left at the finish, and more energy must have been used early in the race, shown by a higher P_{anpeak}. Predicted final times resulting from the different simulated pacing strategies (different anaerobic parameters) for a representative participant are presented in fig 2.
Anaerobic power distribution
In IM, P_{an} changed towards the as optimal predicted direction but did not differ from anaerobic parameters of SP (table 4).
Discussion
A more nearly optimal pacing strategy was imposed on athletes competing at national level: conforming with predictions,1 14 18 a higher initial peak power output was recommended and predicted to lead to better performance. Athletes indeed demonstrated a relatively faster initial pace over the first 300 m, caused by a significant difference in the distribution of P_{tot}, with a higher P_{tot} over the first 300 m. V_{rel} over the last lap was lower, though P_{tot} was not. Though P_{anpeak} and γ changed towards the predicted direction, no differences were found between strategies for these parameters individually. Differences in P_{tot} over time are suggested to be caused by combined effects of changes in P_{anpeak} and γ. However, P_{aer} was assumed to be equal in both SP and IM but is apparently influenced by starting effort23: a larger peak power over the first ∼15 s of the race accelerates V̇O_{2} response. Though the start of IM was faster, SP was already performed with a relatively fast start, so this is not expected to greatly influence results. V̇O_{2} Unfortunately, response was not obtained during the experimental trials, because athletes could not wear the measurement system during competition. A separate trial was performed, where final times were somewhat slower than during the experimental trials, probably attributable to extra air resistance and breathing resistance from the metabolic system. Because all trials were performed at supramaximal effort, this is not expected to largely influence the results.
Another issue in modelling the speed skating performance is that though speedskating race performance can be accurately predicted based on data per 100 m,15 the modelling of speedskating performance based on relatively few data points leads to an error compared to the observed performance (table 5). Unfortunately, it was not possible to measure power output directly on a second by second basis, as in cycling.1 P_{an} was thus fit based on measurement once per 100 m. This affected the determination of total mechanical power output, particularly in the first 100 m, where acceleration was large. Consequently, the estimation of the anaerobic parameters that were varied to simulate different pacing strategies was affected. Based on secondbysecond highspeed recorded velocity profiles obtained at the 1500m Salt Lake City Olympics, this was corrected for. The modelled SP time trial had a final time of 116.27 (4.42) s (about 0.88 s slower than observed performance), and the modelled IM time trial had a final time of 118.11 (3.54) s (about 0.81 s slower than observed performance). Deviations of observed performance were equally large over the first 300 m (table 5) and were not expected to influence differences between trials. Anaerobic parameters were in the same range as cycling and speed skating,1 15 and differences with previous studies were as expected based on the level of the athletes and differences between the sports. Velocity profiles had about the same error over the race; thus, pacing strategy was represented well, though model performance was absolutely slower than observed performance.
Lastly, it has to be raised that only seven participants participated, a consequence of having the unique opportunity of measuring national level elite athletes. For the modelling, this is enough, but performing conventional statistics to compare results is more difficult. Though this has to be kept in mind in interpreting the results, we believe that in combination with modelling, we obtained valuable information on optimal performance in elite athletes.
Though pacing strategies shifted towards the predicted profiles, athletes could not be drawn far from their selfselected pacing strategy. This is in agreement with Hulleman et al,26 showing that SP performance was rather stable and a monetary incentive did not change pacing strategy compared to nonincentivised trials. Athletes seem to have a very robust system of determining pacing strategy, based on endpoint knowledge, previous experience,3 9 10 physiological condition1 27 and peripheral factors.3 27 Based on the awareness of metabolic reserves and homeostatic disturbance in the current exercise bout, this predetermined template is compared to previous experiences, a process that is not easily overridden.26 Though ratings of perceived exertion would have been a valuable addition to the present study, recording RPE would distract the athletes from performing optimally. It could be expected that RPE followed a predetermined exercise template with a growth proportional (scalar) to the estimated event distance6 in a SP trial. By overriding this template by imposing a faster start, ratings of perceived exertion would be expected to increase sooner, and a difference between IM and SP would be expected.
Another point is that more than two training sessions may be necessary to “learn” a different pacing pattern. During an entire speedskating season, no differences in Wingate test performance were found,28 suggesting little change in the anaerobic contribution in the initial part of the race. Over a longer period of time and training (four seasons), successful speed skaters distinguished themselves by the ability to produce higher peak powers.25 Though natural growth was involved, it also suggests that it might be possible to “train” optimal pacing strategy.
Though P_{an} shifted towards a theoretically optimal pacing profile, IM was ∼2 s slower than SP. Mean v_{abs} was higher for SP compared to IM, mainly because velocity was maintained better in the latter half of the race. The worse results for IM could not be explained by differences in mean power output between trials. Differences in motivation were assumed to be minor because there were no differences in total work. External conditions (barometric pressure and ice friction) varied slightly in favour of SP and could account for only part of the difference between performances. The explanation for the worse performance for IM thus must be found in the technical nature of speed skating; a predefined movement where maintaining body position is of large importance and body weight has to be carried continuously. It is important to avoid early fatigue, because loss of technical ability results in a postural change, increasing frontal area and thus increasing air friction coefficient (k_{air}). A horizontal trunk position and a small angle in the kneejoint during gliding reduce air resistance and are important for a proper timing and direction of the pushoff.29 As athletes fatigue throughout the race, body position changes towards a less crouched position,15 which may lessen the restriction of blood flow to the thigh muscles30 but is less favourable aerodynamically. The present study showed that in IM, k_{air} was higher throughout exercise, possibly caused by the summation of increased knee and trunk angles, though individually, these were not differing. It seems that the effort of increasing initial pace results in unfavourable technical changes in speed skating, overriding the potential benefit of the faster start. Additionally, although data are limited, the assumption of a constant GE throughout the entirety of both skating trials is suspect. Given the difficulty in producing an effective push off in speed skating, it is almost inevitable that GE decreases throughout competition, particularly in situations such as IM where fatigue would reasonably interfere with the coordination required to produce the push off efficiently. As discussed, athletes seem to have a welldeveloped sense of physical condition,27 and metabolic rate can be accurately adjusted by a feedback control system.9 By comparing their awareness of metabolic reserves and homeostatic disturbance in the current exercise bout relative to previous experience, a wellevolved system seems to exist, making it possible for the athlete to skate a 1500m without losing the technical ability.
Conclusion
Experienced athletes seem to possess a welldeveloped performance template, and changing pacing strategy towards a theoretically optimal fast start protocol did not result in better performance. Earlier fatigue seems to affect technique; slight deviations from the template apparently have relatively large consequences, because timing and direction of the push off and maintaining body position are of large importance for optimal speedskating performance.
What is already known on this topic
Modelling techniques have resulted in reasonably accurate predictions of performance in both cycling and speed skating. By varying anaerobic energy distribution, different pacing strategies can be evaluated to predict optimal performance. So far, predicted optimal strategies have never been tested in reallife settings by actually imposing them.
What this study adds
The present study was designed to test modelling predictions by actually imposing a theoretically optimal pacing strategy. The results suggested that the predicted optimal degree of “allout” start was not ideal in practice, perhaps because of an unaccounted for effect of fatigue on skating technique.
Acknowledgments
The authors would like to thank the Calgary Olympic Oval, the Sport Science Association of Alberta, the speed skaters participating in this project and their coaches for their cooperation. The authors would also like to thank Shane Esau and Jared Fletcher for their technical assistance.
References
Footnotes

Competing interests None.
Ethics approval The approval was obtained from the University of Calgary Conjoint Health Research Ethics Board.
Provenance and peer review Not commissioned; externally peer reviewed.
Patient consent Obtained.