The “Lambert Submaximal” (LSCT) Sunday Morning Bike Warm Up

At Prairie Performance Triathlon, we’ve used the same warm up protocol for our long brick indoor weekend workouts for the many years. Every year, we get a few questions about why we use this warm up protocol and what the value of it is. So here are a few details and a couple of links to help you along!

Although heart rate information can be challenging to interpret, we’ve learned a few things from regularly using this warm up protocol:

-The LSCT can predict performance!

-The LSCT can be useful in predicting fatigue and over-reaching.

Some easy things to observe after performing the LSCT in a workout:

-How does your Rate of Perceived Exertion (RPE), compare to previous attempts? Is this week harder or easier at the same power or HR?

-Are you able to get your HR up to the 60%, 80% and 90% effort levels? How do the legs respond? Like wise if you’re performing the LSCT by power, is your HR the same at the given power levels?

-Following the 15 minute challenge: Does your HR drop quickly or slowly?; Does it fall within your baseline?

-Does your HR Drop 5-10 beats below your baseline? This could indicate that the body requires additional rest and that the parasympathetic nervous system is more activated.

-How is you mood/mental state compared to other workouts?

-When an athlete is in an over-reaching state, often peak power will decrease and heart rate will be much slower to respond both going up between stages in the LSCT and down on the recovery.

-From a fitness perspective, as training progresses through the season you should start to see either a lower HR at the same power, or higher power at the same HR during the first couple of 6 minute efforts in the test.

As with any type of training or protocols, by regularly performing some type of test or set, an athlete will be able to observe trends in their fitness and recovery state. How much you read into these is totally up to each individual, but it can be very helpful in guiding the athlete towards making good training decisions.