KOM Informatics: Secrets Of The High Responders: Interval Programs Best Practices

By David Brown

KOM Informatics is a web-based application that gives serious cyclists world class tools that provide accurate and comprehensive views of their training workload and how that workload relates to peak performances. We've published a number of articles that focused on the correlation between various aspects of an athlete's workload and their performance in 16 different personal record "time boxes". Here is the first article in the series:KOM Informatics: Raising 20 Minute Power Over 4 Weeks: What Actually Worked.

Just focusing on this first article for now, the findings were that Aerobic intervals (Z4, Z5) dominated the successful interventions, accounting for 52% combined. Anaerobic workloads (Z6, Z7) accounted for a not insignificant 28% which probably reflects the popularity of HIIT modalities.

All of the KOMInformatics athletes that managed successful interventions by achieving a statistically significant correlation between workload and performance did a good job. But some did better than others, judging by the number of watts they improved from the fifth highest to the highest PR in a given time box (WattsDiff). We're calling these athletes high responders. There are potential explanations for this apparent discrepancy that fall outside the data domain of the system. For example, the system has no idea whether an athlete is towards the beginning of their career and thus is more likely to achieve big gains. Since the system can't measure things like this, they won't be addressed in this article. Instead we'll be looking at the ways the most successful athletes implemented their interval programs.

To achieve this, we'll be performing a series of correlation analyses similar to those we did for the original article. Rather then focusing on one athlete at a time, we'll be looking at everyone. The larger sample size should help to reveal trends that might be hard to spot otherwise. One end of the correlation analysis will be focusing on WattsDiff, that is the difference between the 5th highest and highest PR in a given time box. Here is a look at my data from 2022 and 2023 which should clarify things:

The other end of the correlation analysis will focus on total time, and total kj at interval intensity, along with the number of interval sessions for Z4, Z5, Z6 and Z7 intervals. Does putting in more time and more kilojoules at interval intensity imply correspondingly higher watts gain? Do more interval sessions in the study timeframe imply those athletes doing more will achieve proportionate watts gains?

Key Takeaways

The findings presented here suggest that Zone 4 and Zone 7 interval training are better at helping the athlete continue to accrue gains compared to Zone 5 and Zone 6 work. To fully understand why these zones yielded the strongest correlations—and why Zone 5 and Zone 6 did not—we’ll now examine the data in greater depth.”

The following tables highlight the statistically significant correlations between interval workloads and watts gained. Zone 4 and Zone 7 show the strongest positive relationships, while Zone 5 and Zone 6 exhibit weaker, negative, non-significant correlations

Zone 4

0.2894 r(49) = 0.2894, p < .05 (statistically significant) (Count Of Interval Days)

0.2843 r(49) = 0.2843, p < .05 (statistically significant) (Interval Sum Of Seconds)

0.3496 r(49) = 0.3496, p < .05 (statistically significant) (Interval Sum Of KJ)

Zone 5

-0.0567 r(57) = -0.0567, p > .05 (not statistically significant) (Count Of Interval Days)

-0.0053 r(57) = -0.0053, p > .05 (not statistically significant) (Interval Sum Of Seconds)

-0.0112 r(57) = -0.0112, p > .05 (not statistically significant) (Interval Sum Of KJ)

Zone 6

-0.0454 r(68) = -0.0454, p > .05 (not statistically significant) (Count Of Interval Days)

-0.0159 r(68) = -0.0159, p > .05 (not statistically significant) (Interval Sum Of Seconds)

0.0136 r(68) = 0.0136, p > .05 (not statistically significant) (Interval Sum Of KJ)

Zone 7

r(70) = 0.0709, p > .05 (not statistically significant) (Count Of Interval Days)

r(70) = 0.5926, p < .05 (statistically significant) (Interval Sum Of Seconds)

r(70) = 0.6034, p < .05 (statistically significant) (Interval Sum Of KJ)

Discussion

Earlier articles in this series have all provided evidence that Z4, Z5, Z6, and Z7 interval work have all correlated with statistically significant watts gains across the spectrum of PR time bucket durations, for individual riders. But it is clear from the results listed above that increased workloads across the entire KOMInformatics population, don't always yield increased results when viewed through the lens of interval workloads by zone.

Zone 4 interval work yielded statistically significant correlations between workload and performance in all 3 categories tested, whereas Zone 7 work showed favorable results in Interval Sum Of Seconds, and Interval Sum Of KJ.

On the other hand, Zone 5 had non-significant negative correlations between workload and performance in all 3 categories tested, whereas Zone 6 work had non-significant negative correlations with respect to Count Of Interval Days and Interval Sum Of Seconds and a non-significant positive correlation with respect to Interval Sum Of KJ.

When viewed through the lens of the Coggan Adaptations chart ¹, coupled with the accrued wisdom of cycling coaches, and the results of peer-reviewed papers, we can get a good start on explaining the above findings.

Z4 interval work, according to the chart, is the most efficient at triggering peripheral adaptations such as increased mitochondrial enzymes, increased lactate threshold and hypertrophy of slow-twitch muscle fibers. It's pretty much an article of faith amongst cycling coaches that peripheral adaptations are largely dependent upon training stimuli and are less dependent on genetic ceilings compared to central adaptations, and studies have backed up the claim. One study² explored how training intensity and volume impact mitochondrial function and content, finding that mitochondrial function improves significantly with higher-intensity training, while mitochondrial content is more influenced by total training volume. This suggests that athletes may continue to see gains in mitochondrial efficiency even after VO2Max plateaus. Another article³ on mitochondrial adaptations to endurance training discusses how mitochondria are highly adaptable to metabolic stress and exercise, with genetic factors playing a role but not necessarily imposing strict limitations. This supports the idea that mitochondrial enzyme activity may remain trainable for a longer period, and provides a good explanation for why increased work in terms of KJ, time, and interval days continued to yield increased results.

Coaches prescribe Z5 intervals in order to trigger central adaptations such as increased VO2Max and increased stroke volume/maximal cardiac output. The Coggan chart shows this zone as the most efficient at triggering these adaptations. Cycling coaches and self-coached athletes have found that this approach works up to a point, but further improvements plateau rapidly. Research backs up this wisdom. One systematic review⁴ identified 97 genetic variants associated with VO2Max trainability, highlighting that individuals with certain genetic profiles respond better to aerobic training than others. Another article⁵ stated that VO2Max has a strong genetic component, around 50%. The slightly negative, non-significant correlation between increased zone 5 work and PR performance can be explained by athletes hitting this genetic wall.

The Coggan chart shows that Z6 intervals are the best intensity for increased anaerobic capacity (lactate tolerance). However physiologists such as Rønnestad ⁶, and Vaccari⁷ have used Z6 intervals in developing protocols to improve VO2Max. KOMInformatics users have adopted these protocols in their training, but there is no evidence that such training avoids the genetic wall imposed by VO2Max trainability. To the extent they are relying upon these Z6 HIIT protocols, the slightly negative, non-significant correlation between increased Z6 work and PR performance can be explained by athletes hitting this genetic wall. Traditional Z6 intervals outside of the context of raising VO2Max can be a double-edged sword. While they might raise lactate tolerance, too many of these intervals run the risk of upregulating glycolytic adaptations, and shifting muscle recruitment patterns towards fast-twitch muscle fibers. This in turn, can have the effect of suppressing endurance-based adaptations. This sword cutting both ways could potentially explain the slightly negative, non-significant correlation between increased zone 6 work and PR performance for athletes performing traditional Z6 intervals.

The Coggan chart shows that Z7 intervals are the best intensity of increasing neuromuscular power and hypertrophy of fast-twitch muscle fibers. Neuromuscular power is the ability to generate maximum force in a very short duration, typically lasting less than 15 seconds. It relies on the ATP-PC energy system, which provides energy anaerobically using stored adenosine triphosphate (ATP) and creatine phosphate. This system is the fastest at delivering energy to working muscles but depletes quickly, requiring recovery before it can be used again.

Z7 efforts rely on stored ATP and creatine phosphate, bypassing glycolysis entirely. This seems to avoid the risk of upregulating glycolytic adaptations, which can supress endurance-based adaptations. If the athlete couples Z7 training with plenty of endurance training, this would increase the likelihood of fast-twitch fatigue-resistant muscle fibers developing. These fibers, (Type IIa) are unique because they combine explosive power with moderate endurance, making them adaptable for both anaerobic and aerobic efforts. This development could potentially explain the positive, significant correlation between increased zone 7 work (both KJ and duration) and PR performance. Another article in this series gives a breakdown on which PR timeboxes were correlated with various categories of interval work. It's very clear from these results that Z7 work was associated with gains in PR timeboxes from sprint all the way to multi-hour endurance. The correlation between increased zone 7 interval days and PR performance was, positive, but non-significant. This is probably due to the report not looking for a minimum threshold for what counts as a Z7 interval day. A day with just 1 Z7 sprint counted, but likely wasn't a strong enough stimulus to trigger any adaptations.

Takeaways

Even those athletes that have successfully raised PR performances are still tasked with managing the rest of their season in terms of maximizing performance gains. The findings presented here suggest that Zone 4 and Zone 7 interval training are better at helping the athlete continue to accrue gains compared to Zone 5 and Zone 6 work. Coupled with the endurance focused findings from this earlier high responders article, the two articles provide an empirically-backed framework for optimizing watts gains across an entire season.

References

1. Allen, H., Coggan, A. R., & McGregor, S. (2019). Training and racing with a power meter (3rd ed.). VeloPress.

2. Bishop, D. J., Granata, C., & Eynon, N. (2013). Can we optimise the exercise training prescription to maximise improvements in mitochondrial function and content? Biochimica et Biophysica Acta (BBA) - General Subjects, 1840(4), 1266-1275. https://doi.org/10.1016/j.bbagen.2013.10.012

3. Van Heerden, Z. (2025). Mitochondrial adaptations to endurance training. Centre for Integrative Sports Nutrition. Retrieved from https://intsportsnutrition.com/articles/integrative-sports-nutrition/mitochondria-adaptations-to-endurance-training/

4. Williams, C. J., Williams, M. G., Eynon, N., Ashton, K. J., Little, J. P., Wisloff, U., & Coombes, J. S. (2017). Genes to predict VO₂max trainability: a systematic review. BMC Genomics, 18(8), 831. https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-4192-6

5. Klevjer, M., Nordeidet, A. N., Hansen, A. F., Madssen, E., Wisloff, U., Brumpton, B. M., & Bye, A. (2022). New genetic determinants of VO₂max-level identified by GWAS: The HUNT Study. Cardiovascular Research, 118(Supplement_1), cvac066.013. https://academic.oup.com/cardiovascres/article/118/Supplement_1/cvac066.013/6605381?login=false

6.Rønnestad, B.R., Hansen, J., Nygaard, H., & Lundby, C. (2020). Superior performance improvements in elite cyclists following short-interval vs effort-matched long-interval training. Scandinavian Journal of Medicine & Science in Sports, 30(5), 849-857. https://doi.org/10.1111/sms.13627

7.Vaccari, F., Giovanelli, N., & Lazzer, S. (2020). High-intensity decreasing interval training (HIDIT) increases time above 90% VO₂peak. European Journal of Applied Physiology, 120(11), 2397-2405. https://doi.org/10.1007/s00421-020-04463-w