Running plans have come a long way. Back in the day, if you weren’t working with a coach, you’d just get a training plan from a back issue of Runner’s World or your local library’s copy of The Complete Book of Running by Jim Fixx, and have at it with nothing more than a stopwatch and a tracksuit for 16 weeks.

Now, we have plans freely accessible online, the best running watches to provide professional-level guidance and pace information, and adaptive AI-tailored plans from the best fitness apps like Runna. Things have certainly changed, and it’s no longer just about crossing the finish line. Now it’s about posting your time on Strava with a pic of your medal.

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Injured runner holding knee in close-up

(Image credit: Getty Images / milorad kravic)

Noted running blog site (and Garmin leaker) the5Krunner weighed in, highlighting some of the existing research on AI-generated running plans and giving their own opinions as a running expert.

While the5Krunner notes that no peer-reviewed study has yet established that Runna athletes get injured at higher rates than those using a static or traditional training plan, they also point out that “AI training plans are not yet ready for complex scenarios applied at scale to general populations”.

These plans are often aimed at beginner runners, who don’t yet understand the limits of their own bodies. If you’re not a seasoned runner, it’s tougher to gauge whether the plans are too aggressive, which could lead to injuries such as stress fractures, shin splints, pulled muscles or illness. While the only peer-reviewed study the5Krunner cites involves ChatGPT, not Runna, the concern is still valid.

I reached out to Lily Canter, England Athletics running coach and author of Ultra Women: The trailblazers defying sexism in sport (plus frequent TechRadar articles), to see what she thought of AI apps versus static training plans.

Sole of trail running shoe

(Image credit: kovop58 / Shutterstock)

“Whether it’s AI or an off-the-shelf plan, it’s not going to be able to tell whether that person is tired, if they’ve had a stressful work week, if they’ve been eating really badly, if they’ve been sleeping badly”, says Canter. “Sleep data on wearables is still really sketchy.

“Every runner will have a different running experience, different lifestyle, different diet, different stresses at work and home, different goals. As a result, an off-the-shelf plan is never going to cater to any of that. A lot of the beginner plans that you get are not suitable at all – both AI app ones and generic ones from books, websites or magazines.

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“You do need to have quite a lot of running experience with these kinds of AI services to know what information to give it.”

Canter correctly identifies the fact that this is not a new phenomenon: people were overtraining well before AI came into the mix, and there’s significantly more information now, readily available to the average runner, than there was even 20 years ago. Understanding how to train for a marathon is now easier than ever – although it’s obviously still a very hard thing to do.

Canter also picks up on the inherent data biases used in AI’s corpus of training data. “The other big issue with any kind of plan is that they tend to be designed on data that has been collected in sports science on male athletes, and they do not cater for women at all.”

Again, people’s bodies have different needs, and taking a plan as gospel means you’re at risk of overtraining.

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