TrainerRoad Adaptive Training Review: The Future of Faster

TrainerRoad is a bit of an outlier in the universe of cycling training apps. It lacks the candy-hued gamer bling of Zwift, the off-beat humor and array of riding options that come with Systm, and the personal touch of a human coach (which comes with a hefty monthly cost) on Training Peaks. But the platform is highly effective at delivering on its singular mission: to make you a faster cyclist.

The platform achieves this through its machine-learning tool called Adaptive Training, a system that creates goal-based training plans that are updated daily using machine intelligence software that responds to the rider’s unique strengths, weaknesses, and scheduling constraints. The program analyzes every workout by measuring how easily the rider completes each training zone. 

If, for example, you crush a VO2 max workout, the program will adapt and spit out a more difficult workout option the next day. Or on a day when riding feels tough, the program will cut you some slack and provide a slightly less intense follow-up workout. You have the option to accept the adapted program or stick with the original level of difficulty. The more you use it, the more data it can use to fine-tune your training, sort of like a Google Nest thermostat that, over time, finely tunes the temperature of your house by studying your daily use patterns. Since it tracks you over time, it’s sold as a subscription service; you pay $20 a month, or $189 if you buy a whole year at once.

To get started, TrainerRoad creates a customized training plan to help you prepare for a future race, ride, or event. It asks you, among other things, to pick the type of race (gravel, mountain, road), the date of the event, and your preferred indoor and outdoor workout days. For those with no competitive goal in mind, who are just interested in building their fitness, there’s also the TrainNow option in which TrainerRoad allows you to choose from a selection of daily workouts from three categories: Climbing, Attacking, and Endurance.

Adaptive Training may be smart, but it’s still not smart enough to eliminate the need for ramp tests to establish your baseline “functional threshold power” (FTP). This indication of the highest average power you can sustain over the course of 45 to 60 minutes is measured in watts. These FTP tests are incorporated into the training plan at the beginning of the experience, and then you’re retested every four to six weeks in order to recalibrate the program based on your “progression levels.” These progression levels are the way the app tracks your growing fitness across each training zone. Determined on a scale of 1 to 10, they are calculated using three methods: machine learning, the company’s already extensive set of anonymized data gleaned from millions of completed workouts by other athletes, and your own recent workout performance.

TrainerRoad’s software can sync to just about any smart trainer or the power sensors on your bike.

Photograph: Kody Kohlman/TrainerRoad

TrainerRoad’s Adaptive Training appealed to me. In my testing, I found it to be efficient, cost-effective, and easy to use. I was also inspired by the podcasts the company produces. I listened to episodes with users including Masters national champion Jessica Brooks, a busy mom with a full-time, high-level job; US Paracycling Nationals silver-medalist Francesco Magisano, who is blind; and David Curtis, a mountain biker who went from his couch to a sub-nine-hour Leadville 100 in nine months.

I tested the app in a Minnesota December after coming off a four-week cycling hiatus due to minor surgery. With no serious training goal in mind, I established an imaginary 100-mile gravel race for the end of May as my target goal. I did my ramp test in the recommended Erg mode; short for ergometer, this is a mode commonly found on cycling trainers where you let the trainer set the amount of resistance for you based on your pedaling output. During my test, there was a point at which pedaling was so easy that I couldn’t spin fast enough to keep up with the baseline wattage. 

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