Get access to everything we publish when you new study in Medicine & Science in Sports & Exercise , from a research team led by Olli-Pekka Nuuttila of the University of Jyväskylä in Finland and funded in part simply by the sports tech company Polar, seemingly takes the big step toward this goal. Runners, they find, do indeed get faster when they feed heart rate plus other data into an algorithm that tells them when to drive their training harder and when to ease up. At least, they get quicker compared in order to following an inflexible cookie-cutter training plan. The results are intriguing, but these people also leave you wondering if there might be a middle way.
The idea of tech-guided personalized training plans has been gaining steam for at least a decade, most notably with the rise of heart-rate variability (HRV) as a marker of recovery. I wrote about research into HRV-guided training back in 2018, and a person can dig into 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 sympathetic (“fight or flight”) and parasympathetic (“rest and digest”) branches of your nervous system. All else being equal, a high HRV suggests you’re well recovered plus likely to respond nicely to a hard training day.
One of my critiques associated with 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 particular relevant factors that should guide your teaching decisions. Nuuttila’s new study addresses that will critique by using three inputs to make training decisions:
- HRV : averaged over a four-hour period during sleep. If you’re more than half a standard deviation away from your four-week average, that’s a red flag.
- Heart rate-running speed index : if your own heartrate in a given running speed is unusually high compared to your two-week average, that’s another red flag.
- Perceived fatigue and muscle soreness : each assessed on a scale from 1 in order to 7, with 4 becoming normal. If either will be greater than 5, or if the sum of both is 10 or greater, that’s a red flag.
For fifty percent the group, training was adjusted twice the week, on Mondays plus Thursdays. Joggers who had no red flags increased their training load (either distance or intensity, depending upon the education block) for the following three or four days; those who had one red banner kept it the same; and those who had two or three reduced their own load. The other half received a preplanned training program.
The study involved 30 recreational runners that typically trained a little more compared to four times a week. They started with three weeks of maintaining their usual training, then six weeks in which they will focused on increasing their particular training range, followed by another 6 weeks within which they increased their intensity by incorporating one in order to three interval workouts (6 x 3: 00 along with 2: 00 recovery) per week. They completed a variety of performance tests after each instruction block, including a 10K time trial.
There are lots of moving parts in a study like this, but if we scan down to the bottom line, all of us see this particular: the pre-planned group improved their 10K times simply by 2. 9 percent, on average, which is usually about what you’d expect. The algorithm group improved their own average occasions by 6. 2 percent, which is very impressive. Here’s what the individual improvements look like regarding the predetermined (PD) plus individualized (IND) groups, with negative numbers representing a greater percentage improvement:
Interestingly, the total training loads between the particular two groups were really similar, so it’s not simply that one team trained way harder than the other in aggregate. Instead, the differences were individual. When the formula adjusted exercising, it prescribed no change 55 % of the time, a lot more training 35 percent associated with the time, 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” schooling plans, how about “predetermined plus common sense” training programs, where you back off your training if you’re feeling tired and ramp it up if a person feel good? Or if you want to formalize this, take the particular subjective measures of fatigue and tenderness, perhaps along with some measure of perceived effort versus expected effort during the previous workout, plus use that to adjust training weight. There’s plenty of evidence, after all, that our subjective perceptions can be surprisingly sensitive within detecting recuperation 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 to feel like, and intended for experts, who else may be tempted to lie to themselves in order to get the green light to hammer. Yet let’s be clear regarding what we’re testing. The particular nuances of the heart rate and its variability might be useful. But the deeper insight through Nuuttila’s research may simply be that even the best-laid training plans sometimes need to become tweaked.