Fitness tracking took a leap forward in the early 1960s, when the Japanese company Yamasa released a pedometer called Manpo-kei, which roughly translates as “the 10,000-steps system.” This early wearable helped establish the myth that there’s an optimal number of steps you should take every day. Decades later, even as GPS and app-based tracking have proliferated, our increasingly sophisticated devices are often little more than counting tools that log activity. The newest innovations, by contrast, promise to adapt with you, using past performance data to craft customized workouts using machine-learning algorithms. It’s almost like having an automated personal trainer.
For the uninitiated, artificial intelligence is the much hyped term for computational systems designed to mimic human reasoning. Wearables that incorporate AI, in addition to logging activity, can replace human decision-making, using algorithms to find patterns and make predictions. In theory, the more data collected, the better the algorithm becomes at customizing your training. (These so-called recommender systems are not unique to the fitness world, of course. They’re also how Netflix turns your viewing habits into a list of suggestions.) Personalized workout algorithms make up a small but growing portion of the fitness-app space, which is expected to eclipse $15 billion by 2026. And there are signs that the trend is going mainstream: even Planet Fitness, the budget-friendly gym chain, has expressed interest in AI to adjust members’ routines according to performance gains and goals.
This spring, feeling bored with my own routine, I decided to give digital coaching a try. I installed Freeletics, which with 30 million users is one of the more popular apps claiming machine-learning capabilities. The Munich startup has a large client base in Europe and last year raised several million dollars to expand its reach in North America.
The app offers a menu of individual exercises as well as a handful of signature workouts; Prometheus, for example, is a core and lower-body circuit featuring push-ups, mountain climbers, sit-ups, squats, and jumping jacks. There’s also the option to join a premium subscription service that gives the user access to a feature called Coach, which uses an algorithm to devise an individualized training schedule. (Three months costs $35.)
I signed up for the premium version. An initial assessment consisted of timed high-intensity interval exercises—no equipment required. The app cycled through a series of moves (each with a video tutorial) and tracked how long I took to complete each. Afterward it asked me to rate my level of exertion: “I can do even more,”“It was OK,” or “It was too much.” I was prompted to do the same for my form, rating myself on a scale from poor to excellent. Factoring in the time I took to complete these benchmark activities, as well as my subjective self-assessment, Coach built a roughly 30-minute custom workout plan. (The app can put together a series of plans, based on how many days a week you exercise, and allows you to exclude muscle groups if you have an injury or need a rest.) As I sped through squats and took breaks between sit-ups, I couldn’t tell how closely the recommended workouts had been geared toward my initial benchmarks. But right off the bat, Coach encouraged me to do exercises, like burpees, that I have trouble motivating myself to do.
For comparison I downloaded a few similar apps, though I didn’t try anything that required stand-alone hardware such as a Fitbit or smartwatch. Most begin with a baseline assessment or calibration workout and request some general information, such as weight, gender, and fitness goals. Keelo ($31 for three months) offers a CrossFit-like challenge that focuses mostly on high-intensity interval training with an optional weight-lifting component. Fitbod ($60 for a year) is tailored almost exclusively to lifting weights and accounts for available equipment while targeting specific muscle groups. Some apps, such as Kudos ($95 per month), generate personalized regimens but also put you in touch with a human trainer as part of the subscription fee. Still others are geared toward specific activities: Podium ($55 for 14 weeks), for example, helps runners hit a target pace and distance.
According to Julian McAuley, a professor of computer science at the University of California at San Diego, many of the available options are pretty rudimentary. The algorithms tend to be created from global models; in other words, the recommendations are based on how you compare with people of similar age, weight, and gender, rather than being tailored to fit your specific biometric data. But McAuley says that machine learning has the potential to become far more sophisticated. (And because successful approaches to achieving health and fitness vary widely, personalization could be especially useful in a training context.) McAuley’s team has used tracking data to generate customized running routes, which could tell you what distance and elevation gain might bump up your heart rate to a given level. But, he says, “This is really cutting-edge stuff that’s a couple years away from being totally practical.”
I’m still using Freeletics, but only once a week at most. Coach keeps pushing me to do a variety of quick, difficult workouts, and I find that the intensity rarely wavers. This is, I suppose, the whole point of setting goals and having someone hold you accountable. For now, though, Coach doesn’t motivate me quite like a personal trainer might.
Since these apps generally don’t offer much feedback on form or technique, they probably work best as a supplement to advice from a human expert or a group class. And I don’t exactly relish my time with Coach. Freeletics and other apps require you to be plugged in, an obvious downside if, like me, you look forward to your workouts as a respite from screen time. And while these products offer ways to share results on social media (Freeletics also has an in-app leaderboard), in my experience these features pale in comparison with the flesh-and-blood camaraderie I’ve found among fellow gym-goers.