Published May 10, 2020 · Updated Jul 25, 2020
We recap the results of following AI Endurance’s cycling training plan that saw our FTP grow according to AI Endurance’s predictions, following the instructions on how to improve FTP.
We - Dominik and Markus of AI Endurance - have been training for Paris to Ancaster 2020 , a gravel grinder race that was supposed to happen on April 26th 2020 but was cancelled because of Covid-19.
Nevertheless, we stuck to AI Endurance’s 8 week cycling training plan and saw great improvements. If you missed earlier posts about our journey, you can find them here:
Dominik grew his FTP to 283 W in 8 weeks, slightly better than the prediction of 261 W, achieving a new PB with AI Endurance’s individualized training plan. His thoughts:
Markus grew his FTP to 299 W in 8 weeks, also slightly better than AI Endurance’s prediction of 293 W. His thoughts:
Check out Markus’ FTP test on Strava:
Also, see how his FTP improved over time compared to AI Endurance’s predictions:
Don’t waste your time with one-size-fits-all training plans. Use AI Endurance’s predictive data-driven approach instead to improve your FTP and get that PB! Get your own personalized training plan today!
Daily readiness to train is affected by many factors including sleep, illness and training load. Heart rate variability (HRV) readiness to train metrics typically rely on measurements taken immediately upon waking in the morning. We introduce an HRV readiness to train and a durability metric based on DFA alpha 1 (a1) measurements taken during exercise. These new metrics provide additional insights and do not require you to measure HRV upon waking.
We show you in a few simple steps how to connect Suunto Guides with your AI Endurance account. You can get live workout instructions that are optimized to you by our AI. You can also easily determine your training zones and thresholds via a simple ramp test that utilizes your heart rate variability (HRV) data.
DFA (detrended fluctuation analysis) alpha 1 is an HRV (heart rate variability) based aerobic and anaerobic threshold estimation method. It only requires a heart rate monitor that tracks HRV data. It has the potential to track your thresholds automatically without dedicated test workouts.
by Stefano Andriolo. We demonstrate a universal relationship between cycling power and DFA alpha 1 from every day workout data that allows accessible and regular tracking of aerobic and anaerobic thresholds without the need of an exercise lab or even a dedicated testing protocol.
