For access to all of our training, gear, and race coverage, plus exclusive training plans, FinisherPix photos, event discounts, and GPS apps, new study in Medicine & Science in Sports & Exercise , from a research team led by Olli-Pekka Nuuttila of the University of Jyväskylä within Finland and funded in part by the sports technology company Polar, seemingly takes a big step toward this goal. Runners, they find, do indeed get faster when they feed heart rate and other data into an algorithm that will tells them when in order to push their training harder and when to ease upward. At least, they get faster compared to following an inflexible cookie-cutter coaching plan. The results are intriguing, but they also leave a person wondering if there might be a middle way.
The idea of tech-guided personalized teaching plans has been gaining steam for at minimum a decade, most notably with the rise associated with heart-rate variability (HRV) as a marker of recovery. I wrote about research in to HRV-guided education back in 2018, plus you can dig directly into further details about the concept in that article . Briefly, HRV reflects just how regular your own heart beat is, which depends on the balance between the sympathetic (“fight or flight”) and parasympathetic (“rest and digest”) branches associated with your nervous system. All else being equal, a high HRV suggests you’re well recovered plus likely to respond well in order to a hard training day time.
One of my critiques of HRV-guided training—an obvious one—is that instruction (and life) are complicated, so using a single metric to guide your training is unlikely to capture all the relevant factors that will should manual your training decisions. Nuuttila’s new study addresses that critique by using three inputs to make training decisions:
- HRV : averaged over a four-hour period during sleep. If you’re more than half the standard deviation away through your four-week average, that’s a red flag.
- Heart rate-running speed index : if your heart rate at a given running speed is unusually high in comparison to your two-week average, that’s another red flag.
- Perceived fatigue and muscle soreness : each assessed on a scale from 1 to 7, with 4 being normal. If either is greater than 5, or even if the particular sum of both is 10 or greater, that’s a red flag.
For half the group, training was adjusted twice a week, upon Mondays plus Thursdays. Runners who had no red flags increased their exercising load (either distance or intensity, depending on the training block) for the following three or four days; those who experienced one red flag kept it the same; and those who had two or 3 reduced their own load. The particular other fifty percent received a preplanned training program.
The research involved 30 recreational runners who typically trained the little more than 4 times a week. They started along with three weeks of maintaining their usual training, then six several weeks in which these people focused on increasing their schooling distance, followed by an additional six days by which they will increased their particular intensity by incorporating one to three interval workouts (6 x 3: 00 with 2: 00 recovery) per week. They completed a variety of performance tests after each training block, including a 10K time trial.
There are lots of moving parts inside a study like this, but if we scan down to the bottom line, we see this: the particular pre-planned team improved their 10K times by 2 . 9 percent, on typical, which is regarding what you’d expect. The algorithm group improved their own average times by 6. 2 %, which is very impressive. Here’s what the individual improvements look like for the predetermined (PD) and individualized (IND) groups, with negative numbers representing a greater percentage improvement:
Interestingly, the total coaching loads between the 2 groups were very similar, so it’s not simply that one group trained way more difficult than the some other in aggregate. Instead, the differences had been individual. When the algorithm adjusted training, this prescribed no change 55 percent of the time, more teaching 35 percent of the particular time, and less education 10 % of times.
Overall, I think these results are the neat proof-of-principle, and I think it is great that will they’re rigorously testing these types of algorithms to make sure that they really do work better than the alternatives. But I’d like in order to see all of them clear a tougher hurdle. Instead of “predetermined” training programs, how about “predetermined plus common sense” instruction plans, where you back off your coaching if you are feeling tired and ramp it up in case you feel good? Or if you want to formalize it, take the subjective measures associated with fatigue plus soreness, perhaps along with some measure of perceived effort versus expected effort during the previous workout, and use that to adjust exercising load. There’s plenty of evidence, right after all, that our very subjective perceptions can be surprisingly sensitive in detecting recovery status.
It may well be that will having some sort of external, objective data is still useful—both for beginners, who haven’t yet learned what hard training is supposed in order to feel like, and for experts, who may be tempted to lie to themselves in order to find the green light to hammer. But let’s become clear about what we’re testing. The nuances of your own heartrate and its variability may end up being useful. Yet the deeper insight from Nuuttila’s study may just be that even the particular best-laid teaching plans sometimes need to be tweaked.
For more Sweat Science, join me on Twitter plus Facebook , sign up for the email newsletter , and check out my book Endure: Mind, Body, and the particular Curiously Elastic Limits associated with Human Performance .