Thanks for asking and looking forward to your podcast.
Some possible questions and some lead in verbiage:
- The 80/20 discussion in various forums has sparked nice debate on how, and if, amateurs (e.g. weekend warriors, USA cat 3/4 level riders) can adopt and benefit from the same principles.
For amateur cyclists we know many ride about 8-12 hours a week and this time is precious. Many of these riders will have a goal not to “peak” for a single event but to be “good” for a season.
Question 1: Would you suggest an 80/20 work split principles for these athletes pre-season and/or during season?
Question 2: if Yes, what key workouts would you suggest these athletes program? For example: Tuesday 4 x 8 @ 105%, Thursday 2 x 20 @ 95%, Saturday long ride 3-4 hours.
- Physiology and training studies are extremely difficult to perform well, are difficult to control, and are rarely replicated. Your findings on interval duration and “bang for the buck” looking at 4x4 v 4x8 v 4x16 has generated a great deal of interest. Particularly as time restricted athletes are always looking for the best return on their time investment.
Question 1: Do you have plans to replicate this experiment in a separate (new) cohort of athletes?
Question 2: Have you considered a larger experiment where after Training Set 1 (and perhaps a recovery period) re-randomizing the cohort to different interval program?
For example the group that performed 4x8 first would be switched to either 4x4 or 4x16 for Training Set #2, then again for training set #3. The primary question being is 4x8 a “magic” interval set or was it something about the cohort in the experiment that lead to the finding?
Given the difficulty of running these experiments in a single location, have you considered “Crowd Sourcing” volunteers for further studies? While this could increase noise, there are a great many athletes training with power, HR and some with lactate, who would be happy to follow a specific program for a period of time and contribute data to a primary investigator for publication. Taking this approach would allow both real world application of theory (for example SST vs 80/20 or 4x8 v 4x4, etc etc) and real world data on impact of training hypothesis. Am certain there would be no shortage of “qualified” volunteers who would complete the project.
What are your top three pieces of advice for amateurs? Am expecting (1) recovery, (2) consistency, (3) go really hard on your hard day. Defining “really hard” would be useful.
What research are you working on that data geeks can look forward to reading / hearing about next?