TrainerRoad Announces Adaptive Training Open Beta and Free Trial, Providing Cyclists Early Access to the Future of AI-Powered Training
September 16, 2021
TrainerRoad uses machine learning technology, science-based coaching principles and an unprecedented data set to monitor athlete performance and intelligently adjust training programs based on individual needs.
RENO, Nevada (September 16, 2021) – TrainerRoad, cycling’s most complete & effective training system and the market leader in making athletes faster, is excited to announce the open beta availability of their new Adaptive Training system, offering personalized training powered by advanced machine learning for every cyclist. TrainerRoad now offers a 7-day free trial period to new athletes providing unlimited access to every feature of the platform, including Adaptive Training.
“We’re excited to finally give athletes the chance to try TrainerRoad for free,” TrainerRoad Communications Director, Jonathan Lee said. “With Adaptive Training available for new subscribers, you’ll have the chance to sample what training is like with the world’s first machine learning-driven training platform for cyclists.”
Current TrainerRoad athletes will automatically have access to the new feature, revolutionizing the way individuals approach training. Whether cyclists build a custom training plan with TrainerRoad’s Plan Builder or choose workouts as they go with TrainNow, Adaptive Training’s machine learning system continually monitors performance and adjusts to meet the needs of individual cyclists.
“Development of Adaptive Training is progressing rapidly,” Lee said. “With the help of the athletes in our Closed Beta Program, we were able to gather invaluable data and feedback that has further trained the machine learning that makes Adaptive Training so powerful. The result is more productive training for more athletes, making them faster than they’ve ever been.”
Every day, Adaptive Training responds to fitness breakthroughs, real-life scheduling surprises, and athletes’ constantly changing levels of fitness. It then analyzes relative strengths and weaknesses by measuring how workouts are completed in each training zone and recommends specific adjustments to training in response, just as a coach would. Athletes simply need to follow their training plan or choose individual workouts with TrainNow, and Adaptive Training takes care of the details for them.
Adaptive Training does not replace the TrainerRoad Ramp Test or use of FTP as a primary benchmark. In fact, it makes these tests and their results even more powerful by quantifying athletes’ capacity in each training zone with additional nuance. As a result, workouts will align with an athlete’s current abilities and goals more closely than with FTP alone. This also prevents the dramatic increase in workout difficulty that has traditionally accompanied an increase in FTP, and makes upcoming workouts slightly less intense, easing the transition into training at higher FTP.
“As we near a full public release of Adaptive Training, we’re excited to use this Open Beta period to get even more data for our machine learning algorithms, and to offer new subscribers the unprecedented experience of starting their training journey from day one with Adaptive Training,” Lee said.
TrainerRoad is on the cutting edge of making cyclists faster. The addition of Adaptive Training to training programs, structured workouts, and cycling analytics only reinforces the brand’s commitment to improving athlete performance and achievement. For more information on TrainerRoad and to sign up for a 7-day free trial, visit www.TrainerRoad.com.
Click here for Adaptive Training Media Kit
TrainerRoad is the leading training system for cyclists and triathletes who want to get faster. Athletes in over 150 countries use TrainerRoad’s training calendar, apps, workouts, training plans and analysis tools to elevate their performance. Additionally, TrainerRoad’s forum, blog, and podcasts are trusted educational resources for athletes around the world. Learn more at www.TrainerRoad.com.