TrainerRoad Adaptive Training Review: The Future of Faster

TrainerRoad is a A little different in the cycling training app universe. It lacks gamers who like to eat candy Zwift, off-beat humor and a host of riding options included Systmand the personal touch of a human trainer (which comes with a hefty monthly cost) over Training pinnacle. But the platform is highly effective at fulfilling its sole mission: to help you become a faster cyclist.

The platform achieves this through a machine learning engine called Adaptive training, a system that generates daily updated goal-based training plans using machine intelligence software to meet a rider’s unique strengths, weaknesses, and schedule constraints . The program analyzes every exercise by measuring how easy it is for the rider to complete each exercise zone.

For example, if you love your VO2 max workout, the program will adapt and offer a harder workout option the next day. Or on a day when riding is difficult, the program will help ease your slack and provide a slightly less intense follow-up workout. You have the option to accept the adjusted program or stick with the original difficulty level. The more you use it, the more data it can use to fine-tune your training, just like the Google Nest thermostat, over time, adjusts the temperature of your home over time. by researching your daily usage patterns. Since it tracks you over time, it is sold as a subscription service; you pay $20 a month or $189 if you buy the whole year at once.

To get started, TrainerRoad creates a custom training plan to help prepare you for a future race, ride, or event. It asks you, among other things, to choose your race type (gravel, mountain, road), event date, and your preferred indoor and outdoor training dates. For those without competitive goals who are only interested in building their fitness, there is also the TrainNow option, where TrainerRoad lets you choose daily workouts from three categories: Leo mountains, Attack and Endurance.

Adaptive training can be smart, but it’s still not smart enough to eliminate the need for ramp testing to establish your basic “functional threshold power” (FTP). This indication of the highest average wattage you can sustain between 45 and 60 minutes is measured in watts. These FTP tests are incorporated into the training plan at the start of the experience, and you are then retested every four to six weeks to recalibrate the program based on your “progress.” These progression levels are how the app tracks your increasing physical activity across each training area. Identified on a scale of 1 to 10, they are calculated using three methods: machine learning, the company’s extensive anonymized datasets gathered from millions of completed workouts by athletes other and your own recent workout performance.

TrainerRoad’s adaptive training captivated me. In my testing, I found it effective, cost-effective, and easy to use. I am also inspired by podcasts production company. I’ve listened to episodes with users including Masters national champion Jessica Brooks, a busy, highly qualified working mom; Francesco Magisano, National Paracycling Silver Medalist, is blind; and David Curtis, a mountain biker, who went from his couch in under nine hours Leadville 100 in nine months.

I tested the app in December in Minnesota after going through a four-week hiatus from cycling due to minor surgery. With no serious training goals in mind, I set an imaginary 100-mile gravel race in late May as my goal goal. I did my ramp test in the suggested Erg mode; short for ergometer, this is a mode commonly found on cycling trainers where you let the trainer set resistance for you based on your pedal output. During my testing, there was a point where pedaling was so easy that I couldn’t spin fast enough to keep up with base power.