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!
Before every workout you should know if you're actually ready for it. Everyone responds differently to stress, bad sleep and exercise fatigue - our new recovery model makes data driven decisions about when you should train and when you shouldn't - based on heart rate variability (HRV).
Apple Watch has been a popular choice in running and triathlon. You can now track and execute your AI Endurance custom running, cycling and swimming workouts on your Apple Watch.
by Stefano Andriolo, Markus Rummel and Iñigo Tolosa. We present a new real time feature of evaluating readiness to train based on in-activity heart rate variability (HRV) measurements during the warm up of your activity. You can use this feature in the newest version of the alphaHRV Garmin Connect IQ app at no additional cost.
by Stefano Andriolo. Building on previous work, we refine a method to accurately determine the relationship between DFA alpha 1 and power. This method can be used to track fitness and thresholds of an athlete. We find in some cases ramp detection tends to overestimate thresholds, a finding mirrored in recent physiological papers. On the other hand, thresholds based on clustering of DFA alpha 1 values tend to agree well with this new method. We propose a hybrid lab and everyday workout experiment to further study the relationship.
