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Best Of PEZ: Different Strokes For Different Folks?
Time trials are all about “leaving everything on the road.” You want to pace yourself so that you hit the finish line with nothing left in the gas tank. Many strategies for achieving this have been proposed in the scientific literature. The other question to ask is what the effects of ability are on pacing strategy, and whether such strategy is ingrained or learned.

It’s About Time
The time trial is absolute the race of truth, where there is no way and no where to hide. Many feel that it is the purest form of bike racing, because it’s simply each rider against the clock and may the best win. Many times, stage races are dominated by a time trial, allowing dominant specialists such as Miguel Indurain to build such a huge advantage that they merely have to hang tight and not lose too much time in the mountains through the 1991-1995 Tours. Go back even further and you’ll remember that Jacques Anquetil, the first rider to win five Tours, did the damage in the time trials.

More recently, the memory of Fabian Cancellara running amok in the final TT stage in the 2009 Tour de Suisse, crossing the line on his TT bike in a victory salute, should indelibly mark the importance of time trialing ability for any racer. And of course, the crucial stage for this week’s Tour of California is likely the 33 km TT around Los Angeles.

Pacing strategy
One of the continuing themes I’ve had in Toolbox over the past few years has been the idea of pacing strategies in cycling. Last fall, we looked at different pacing strategies over a 5 km effort. Earlier this year, we explored the relationship between pain sensations and TT power output. This research emphasis has arisen from new ideas about how we as humans regulate our exercise effort, and especially about whether we regulate our effort based on a conscious combination of physiological cues and also psychological cues.
Much of the existing research on pacing strategies have used subjects that were generally elite cyclists, with the general scientific rationale that we want to use subjects who have highly repeatable performances. While that is indeed an important consideration, this approach makes it difficult to determine whether pacing strategy is innate or learned – the classic “nature versus nurture” debate. Besides that debate, understanding this question also can help us to explore the best ways of training or teaching pacing strategies to cyclists.

Lima-Silva et al. 2010
A multinational research group (Brazil, UK, and Australia certainly qualifies!) looked at the question: “the influence of the performance level of athletes on pacing strategy during a simulated 10-km running race, and the relationship between physiological variables and pacing strategy.”
Some of the study’s (Lima-Silva et al. 2010) basic details:

Running was used in this study, but the endurance nature of the sport along with the reliance on proper pacing makes it an easy transfer to the sport of cycling and especially time trials.

24 male runners participated across a range of fitness and training histories. All were members of running clubs and ranged from regional to national level competitors. All had been training at least the previous 3 years, and had ran >10 ten-km races in the past two years, with at least one of them <40 min. This certainly does not cover the entire spectrum of untrained individuals who are naпve to exercise or pacing, but was a compromise that they were familiar enough with running to have some sense of pacing and performance.

After some baseline testing, the main test was a 10 km time trial on an outdoor track at Sao Paulo University. Environmental conditions were fairly moderate for both temperature (15.7oC, 80.5% relative humidity), pressure (697.1 mmHg), and wind speed (6.1 km/h).

Oxygen consumption and other metabolic and respiratory data were recorded during the run using a portable metabolic cart (Cosmed) worn by the subjects. Heart rate and lactate measures were also taken.

Subjects were given verbal encouragement but no feedback about times or heart rate. Obviously, they had some innate sense of speed with each lap being 400 m and knowing that they needed to complete 12.5 laps.

Average velocity was calculated based on each 400 m split time.

Following data collection, the subjects were split into three groups based on performance times. The fastest 8 were the “high performance” and the slowest 8 were the “low performance” groups. The middle 8 were excluded from further analysis to create a clear separation between high and low performers.

Go High or Go Low?
Overall, the study was interesting in exploring the strategies of high (HP) versus low performers (LP). The main weakness of the design may be in the subject selection, in that a 10 km time of ~40 min is still definitely a trained and fit individual. Therefore, these “low” performers can definitely not be extrapolated or confused with untrained or sedentary individuals. It also means that the low performers still had a decent degree of experience with pacing.

In the end, this may be more of a scientific quibble that actually works in our favour, as the “low performers” may be analogous to many of us age-groupers or lower category racers, while the “high performers” may be analogous to the Cat 1/2 or top Masters racers.

Some of the key findings between the two groups in baseline:

No real difference in age or height between HP and LP. It wasn’t statistically significant, but the HP group was 61.8 kg and the LP quite a bit heavier at 70.9 kg.

No real difference in VO2max (63.1 vs. 60.0 mL/kg/min). Neither are exceptionally high actually. However, peak velocity on an initial test was much higher in the HP (17.9 km/h) than LP (16.4 km/h). This point to the relative inadequacy of using simply VO2max as an index of fitness or actual performance.

Submaximally, the HP group experienced much less physiological strain and were more efficient at different running speeds. Overall, there was a tendency for lower oxygen uptake and lactate levels at 9, 12, and 15 km/h in the HP than LP.

10 K Results
Not surprisingly, as this was the whole goal of the group separation, the average speeds for the HP (17.2 km/h) was much faster than LP (14.5 km/h), and this was the case throughout each 400 m segment of the entire 10 K race. However, the pacing pattern was distinctly different between the two groups in addition to the speed.

The HP group had a “classic” U-shaped pacing pattern. They started out at a much higher (18.9 km/h) than average speed. This dropped gradually but significantly over the first 2K to 17.4 km/h, and then an end-spurt of 18.3 km/h over the final 400 m.

On average, the HP ran at a higher percentage of their peak velocity and also their velocity at OBLA (onset of blood lactate accumulation or 4 mmol lactate concentration) than the LP group. Therefore, not only were the HP speeds different on an absolute comparison as was their pacing pattern, the effort that they could voluntarily regulate themselves to work at was more stressful than what the LP group could voluntarily handle.

The results are very interesting, in that they suggest (though they do not necessarily prove) two conclusions:

First, even for a longer event like a 10 K run (or a 20-30 km TT), an “all-out” or U-shaped pacing strategy will produce the best performance and times. This extends the prior work we have examined on 5 km cycling time trials, which again goes against traditional TT advice of starting conservatively and building speed gradually. For those of us doing TT with power, the optimal strategy may not be to try to lay out an even wattage throughout the entire race (even-pacing). Rather, experiment in test TTs by going 10-15% above your planned average power for the first 20% of the race.

Second, training for time trials is not just about improving your functional threshold power or lactate threshold by repeatedly doing prolonged efforts at or near your threshold. Rather, it appears important to build your supra-threshold tolerance by doing efforts such as “over-unders” where you go harder than typical TT pace, recover at or slightly below TT pace, and repeat.
Finally, the big unknown in this study remains the chicken or egg dilemma. Are the HP athletes better because they could naturally handle this higher intensity of effort and pacing strategy, or have they trained themselves gradually to do this and have therefore improved their performance? The only way to figure this out is to use ourselves as guinea pigs, so see you at your local TT!

Have fun and ride safe!

Lima-Silva AE, Bertuzzi RC, Pires FO et al (2010) Effect of performance level on pacing strategy during a 10-km running race. Eur J Appl Physiol 108:1045-1053. doi:10.1007/s00421-009-1300-6

About Stephen:

Stephen Cheung is a Canada Research Chair at Brock University, and has published over 50 scientific articles and book chapters dealing with the effects of thermal and hypoxic stress on human physiology and performance. He has just published the book Advanced Environmental Exercise Physiology dealing with environments ranging from heat and cold through to hydration, altitude training, air pollution, and chronobiology. Stephen’s currently writing “Cutting Edge Cycling,” a book on the science of cycling, and can be reached for comments at .


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