TrainerRoad Adaptive Training Review: Fast Future


TrainerRoad is a A bit of the universe of cycling training apps. It features Zwift’s Candy-Hood gamer bling, an array of off-beat humor and riding options that come with the system, and a personal touch of a human trainer (including a huge monthly cost) in training picks. But the platform is extremely effective in delivering its single mission: to quickly build you up as a cyclist.

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

If, for example, you crash a VO2 max workout, the program will adapt and release the more difficult workout option the next day. Or on days when riding seems difficult, the program will give you some relaxation and some less intense follow-up workouts. You have the option of taking the adapted program or sticking to the basic level of difficulty. The more you use it, the more data it can use to fine-tune your training, such as a Google Nest thermostat that subtly protects your home temperature by studying the types of your daily use over time. Since it tracks you over time, it is sold as a subscription service; You pay $ 20 a month, or $ 189 if you buy once a year.

To get started, TrainerRoad creates a customized training plan that helps you prepare for future races, rides or events. It asks you to choose, among other things, the type of race (pebbles, mountains, roads), the date of the event, and your preferred indoor and outdoor workout days. For those who have no competitive goals in mind, who are only interested in building their fitness, there is also the TrainNow option where TrainerRoad lets you choose a daily workout 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 testing to establish your baseline “functional threshold power” (FTP). This indication of the maximum average power you can maintain in 45 to 60 minutes is measured in watts. These FTP tests are included in the training plan at the beginning of the experience, and then you are re-tested every four to six weeks to recalibrate the program based on your “progress level”. These progress levels are the way the app tracks your growing fitness across each training area. Determined on a scale of 1 to 10, they are calculated using three methods: machine learning, an extensive set of anonymous data that the company has already collected from millions of other complete workouts by other athletes, and your own recent workout performance.

TrainerRoad’s software can sync with any smart trainer or power sensor on your bike.

Photo: Cody Kohlman / Trainer Road

TrainerRoad’s adaptive training applied to me. In my experiments, I found it efficient, affordable, and easy to use. I was also inspired by the podcasts produced by the company. I listened to the episode with users, including Masters National Champion Jessica Brooks, a full-time, high-level busy mother; US Paracycling Nationals silver-medalist Francesco Magisano, who is blind; And David Curtis, a mountain biker who went from his couch to the Sub-Nine-Hour Lidville 100 in nine months.

I tested the app in Minnesota in December after stopping a four-week cycling break due to minor surgery. With no serious training goals in mind, I set up a fictional 100-mile pebble race for the end of May as my goal. I tested my ramp in the suggested Erg mode; Short for ergometer, this is a mode commonly found in cycling instructors where you let the instructor determine the amount of resistance for you based on your pedaling output. During my testing, there was a point where pedaling was so easy that I couldn’t spin fast enough to keep up with the baseline wattage.



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