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The techno-utopian vision of the future of endurance training is that on any given day, your workout will be perfectly calibrated for how you’re feeling, how your body responds to different stimuli, and what your current goals are. Wearable tech will track your own workout and monitor your recovery around the clock, and the algorithm may know just how far to push you. A new study in Medicine & Science in Sports & Exercise , from a research team led by Olli-Pekka Nuuttila of the particular University associated with Jyväskylä inside Finland plus funded within part by the sports tech 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 tells them when to push their training harder and when to ease up. At least, they get faster compared to following an inflexible cookie-cutter training plan. The results are intriguing, but they also leave you wondering if there might be a middle way. The particular idea of tech-guided personalized training plans has been gaining steam with regard to at least a decade, most notably with the rise associated with heart-rate variability (HRV) as a marker of recovery. I wrote about research into HRV-guided training back in 2018, and you can dig in to further details about the concept in that article . Briefly, HRV reflects exactly how regular your own heart beat is, which depends upon the balance between the particular sympathetic (“fight or flight”) and parasympathetic (“rest plus digest”) branches of your nervous system. All else being equal, a low HRV suggests you’re well recovered and likely in order to respond well to a hard training day. One of my critiques of HRV-guided training—an obvious one—is that coaching (and life) are complicated, so using a single metric to guide your training is unlikely to capture all the relevant factors that should guide your teaching decisions. Nuuttila’s new study addresses that will critique simply by using three inputs to make training decisions:
For half the particular group, training was adjusted twice a week, on Mondays and Thursdays. Runners who had no red flags increased their training load (either distance or intensity, depending on the education block) regarding the following three or even four days; those that had one red light kept it the same; and those who had two or three reduced their weight. The other half received a preplanned training program.
The study involved 30 recreational runners who else typically trained a little more compared to four times a week. They started with 3 weeks associated with maintaining their own usual training, then six weeks in which they focused on increasing their particular training range, followed by another 6 weeks by which they increased their strength by incorporating one to three interval workouts (6 x 3: 00 along with 2: 00 recovery) per week. They completed a variety of performance tests after every training block, including a 10K time trial.
There are lots of moving parts in the study like this, but if we scan down in order to the bottom line, we see this particular: the pre-planned group improved their 10K times simply by 2 . 9 percent, on average, which is about what you’d expect. The formula group enhanced their typical times by 6. 2 percent, 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 training loads between the particular two groups were very similar, so it’s not simply that one group trained way harder than the other in aggregate. Instead, the differences were individual. When the protocol adjusted instruction, it prescribed no change 55 percent of the time, a lot more training 35 percent of the period, and less training ten percent of times.
Overall, I think these results are a neat proof-of-principle, and I think it’s great that they’re rigorously testing these algorithms to make sure that will they really do work better than the alternatives. But I’d like to see them clear a tougher hurdle. Instead of “predetermined” exercising plans, how about “predetermined plus common sense” training programs, where a person back off your training if you’re feeling tired plus ramp it up if you feel good? Or if you want to formalize it, take the particular subjective measures of fatigue and tenderness, perhaps along with some measure associated with perceived effort versus expected effort during the previous workout, and use that to adjust training fill. There’s plenty of evidence, after all, that our subjective perceptions can be surprisingly sensitive within detecting recuperation status.
It may nicely be that will having some sort of external, objective data will be still useful—both for beginners, who haven’t yet learned what hard training is usually supposed to feel like, plus for experts, who may be tempted in order to lie to themselves in order to get the green light to hammer. But let’s be clear about exactly what we’re testing. The nuances of the coronary heart rate and its variability might be useful. But the deeper insight from Nuuttila’s study may simply be that even the best-laid schooling plans sometimes need in order to be tweaked.
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