4 Ways to Use the Training Data from Wearable Tech

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The central issue that sports scientists are grappling with these times is this: What the heck are we heading to do with all this data? In stamina sports, we’ve progressed from heart rate monitors and GPS watches to subtle biomechanical assessment, inside oxygen amounts, and steady glucose measurements, all shown on your wrist then quickly downloaded to your personal computer. Group sports have undergone a equivalent tech revolution. The ensuing data is intriguing and ample, but is it actually practical?

A new paper in the International Journal of Sports Physiology and Performance tackles this issue and provides an attention-grabbing framework for considering about it, derived from the business enterprise analytics literature. The paper comes from Kobe Houtmeyers and Arne Jaspers of KU Leuven in Belgium, together with Pedro Figueiredo of the Portuguese Soccer Federation’s Portugal Soccer College.

Here’s their four-stage framework for data analytics, offered in buy of each growing complexity and growing price to the athlete or mentor:

• Descriptive: What occurred?
• Diagnostic: Why did it come about?
• Predictive: What will come about?
• Prescriptive: How do we make it come about?

Each stage builds on the prior 1, which implies that the descriptive layer is the basis for almost everything else. Is the data fantastic more than enough? I’m really self-confident that a modern GPS enjoy can accurately describe how considerably and how quick I have run in instruction, which permits me to move to the upcoming stage and consider to diagnose regardless of whether a fantastic or negative race resulted from instruction too much, too little, too challenging, too straightforward, and so on. In contrast, the heart rate data I get from wrist sensors on sports watches is utter garbage (as confirmed by evaluating it to data from chest straps). It took me a when to realize that, and any insights I drew from that flawed data would definitely have been meaningless and quite possibly detrimental to my instruction.

Producing predictions is harder (in particular, as the expressing goes, about the long run). Experts in a wide variety of sports have attempted to use machine understanding to comb through big sets of instruction data to predict who’s at superior chance of having hurt. For instance, a analyze posted before this year by researchers at the University of Groningen in the Netherlands plugged 7 a long time of instruction and injuries data from seventy four competitive runners into an algorithm that parsed chance dependent on either the prior 7 times of jogging (with ten parameters for every single day, like the whole distance in distinct instruction zones, perceived exertion, and length of cross-instruction) or the prior a few months (with 22 parameters per week). The ensuing product, like equivalent ones in other sports, was appreciably improved than a coin toss at predicting injuries, but not but fantastic more than enough to foundation instruction conclusions on.

Prescriptive analytics, the holy grail for sports scientists, is even extra elusive. A straightforward instance that doesn’t involve any major computation is heart-rate variability (HRV), a proxy evaluate of tension and restoration standing that (as I talked over in a 2018 report) has been proposed as a daily guideline for choosing regardless of whether to coach challenging or straightforward. Even though the physiology will make sense, I have been skeptical of delegating very important instruction conclusions to an algorithm. That’s a untrue choice, though, according to Houtmeyers and his colleagues. Prescriptive analytics delivers “decision assistance systems”: the algorithm isn’t changing the mentor, but is furnishing him or her with a different point of view which is not weighed down by the inevitable cognitive biases that afflict human conclusion-producing.

Curiously, Marco Altini, 1 of the leaders in producing approaches to HRV-guided instruction, posted a Twitter thread a several months back in which he reflected on what has altered in the area since my 2018 report. Among the the insights: the measuring technological innovation has improved, as has knowledge about how and when to use it to get the most reputable data. That’s critical for descriptive utilization. But even fantastic data doesn’t ensure fantastic prescriptive suggestions. According to Altini, reports of HRV-guided instruction (like this 1) have moved absent from tweaking workout plans dependent on the vagaries of that morning’s reading, relying as a substitute on more time-time period tendencies like jogging 7-day averages. Even with these caveats, I’d nonetheless watch HRV as a source of conclusion assistance rather than as a conclusion-maker.

A person of the reasons Houtmeyers’s paper appealed to me is that I put in a bunch of time considering about these problems during my latest experiment with steady glucose checking. The four-stage framework will help clarify my considering. It is clear that CGMs provide wonderful descriptive data and with some effort and hard work, I imagine you can also get some fantastic diagnostic insights. But the sales pitch, as you’d assume, is explicitly concentrated on predictive and prescriptive claims: guiding you on what and when to try to eat in buy to improve efficiency and restoration. It’s possible which is feasible, but I’m not but certain.

In reality, if there’s 1 straightforward information I choose absent from this paper, it is that description and diagnosis are not the exact detail as prediction and prescription. The latter doesn’t abide by quickly from the previous. As the data sets keep having larger and greater-excellent, it would seem inevitable that we’ll eventually achieve the level when machine-understanding algorithms can decide on up designs and interactions that even remarkably expert coaches could overlook. But which is a big leap, and data on its own—even “big” data—won’t get us there.

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