Use Zwift custom workouts to grow your FTP with a data-driven, personalized Zwift custom training plan from AI Endurance.
You can now execute your AI Endurance workouts as Zwift custom workouts by following these simple steps:
In case you are updating your training plan make sure to delete all AI Endurance .zwo files from your old plan in the workout folder prior to uploading your new workouts. Don't delete the file workouts.files as it is Zwift's way of keeping track which workouts were deleted.
For more information on how to use custom workouts in Zwift, see What's on Zwift.
That's it, now you can execute your AI Endurance rides as a Zwift custom workouts training plan.
See also our Zwift running workouts.
You can also get a taste of some our workouts under
AI Endurance is based on the observation that an optimal training routine can be very different for each individual. That's why one-size-fits all training plans often don't yield the expected results.
With AI Endurance you get truly personalized, data-driven training based on your accumulated historic power data. Our machine learning algorithm is like a 'digital twin' that represents how you respond to different training routines. This allows us to
For your optimized, personalized training plan we take into account
You can always make adjustments to your training plan when real life gets in the way.
Sign up today and get your own personalized training plan to reach your goal FTP!
Get your AI Endurance best training plan into TrainingPeaks. From there, you can execute your TrainingPeaks workouts in Zwift and many other apps. Connect your AI Endurance account once and any changes will automatically be synced with TrainingPeaks.
by Grant Paling. In his second of three blog posts, Grant shares his personal experiences with AI in endurance training, emphasizing the blend of AI assistance and human accountability in achieving his personal triathlon goals.
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.
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).