November 28, 2022

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4 Ways to Use the Training Data from Wearable Tech

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The central query that sports experts are grappling with these times is this: What the heck are we going to do with all this data? In stamina sports, we’ve progressed from coronary heart amount displays and GPS watches to complex biomechanical assessment, interior oxygen concentrations, and continual glucose measurements, all displayed on your wrist then mechanically downloaded to your pc. Staff sports have undergone a identical tech revolution. The resulting data is interesting and plentiful, but is it essentially handy?

A new paper in the International Journal of Sports activities Physiology and General performance tackles this query and presents an attention-grabbing framework for pondering about it, derived from the business enterprise analytics literature. The paper arrives from Kobe Houtmeyers and Arne Jaspers of KU Leuven in Belgium, together with Pedro Figueiredo of the Portuguese Football Federation’s Portugal Football Faculty.

Here’s their 4-phase framework for data analytics, presented in order of both of those rising complexity and rising value to the athlete or coach:

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

Each phase builds on the earlier one particular, which means that the descriptive layer is the basis for anything else. Is the data great ample? I’m really self-assured that a present day GPS watch can precisely explain how significantly and how quickly I have run in coaching, which permits me to move to the up coming phase and test to diagnose no matter if a great or terrible race resulted from coaching way too a lot, way too very little, way too really hard, way too straightforward, and so on. In contrast, the coronary heart amount data I get from wrist sensors on sports watches is utter garbage (as verified by evaluating it to data from chest straps). It took me a even though to notice that, and any insights I drew from that flawed data would definitely have been meaningless and perhaps harming to my coaching.

Producing predictions is more durable (in particular, as the stating goes, about the long run). Researchers in a wide variety of sports have experimented with to use equipment understanding to comb by way of major sets of coaching data to predict who’s at large possibility of having hurt. For instance, a review published before this calendar year by scientists at the University of Groningen in the Netherlands plugged 7 several years of coaching and personal injury data from 74 competitive runners into an algorithm that parsed possibility based on either the earlier 7 times of operating (with 10 parameters for each individual day, like the overall length in diverse coaching zones, perceived exertion, and duration of cross-coaching) or the earlier a few weeks (with 22 parameters per 7 days). The resulting product, like identical ones in other sports, was appreciably greater than a coin toss at predicting accidents, but not nevertheless great ample to base coaching choices on.

Prescriptive analytics, the holy grail for sports experts, is even extra elusive. A very simple instance that doesn’t demand any weighty computation is coronary heart-amount variability (HRV), a proxy evaluate of anxiety and recovery status that (as I talked about in a 2018 write-up) has been proposed as a every day information for selecting no matter if to teach really hard or straightforward. Even even though the physiology will make feeling, I have been skeptical of delegating very important coaching choices to an algorithm. That is a bogus decision, even though, in accordance to Houtmeyers and his colleagues. Prescriptive analytics gives “decision guidance systems”: the algorithm is not replacing the coach, but is furnishing him or her with a different perspective that’s not weighed down by the unavoidable cognitive biases that afflict human choice-making.

Interestingly, Marco Altini, one particular of the leaders in acquiring strategies to HRV-guided coaching, posted a Twitter thread a number of weeks in the past in which he mirrored on what has transformed in the field considering the fact that my 2018 write-up. Amongst the insights: the measuring technologies has enhanced, as has know-how about how and when to use it to get the most responsible data. That is essential for descriptive utilization. But even great data doesn’t assurance great prescriptive assistance. According to Altini, experiments of HRV-guided coaching (like this one particular) have moved away from tweaking work out strategies based on the vagaries of that morning’s studying, relying in its place on longer-phrase trends like operating 7-day averages. Even with those people caveats, I’d however see HRV as a supply of choice guidance alternatively than as a choice-maker.

A single of the motives Houtmeyers’s paper appealed to me is that I invested a bunch of time pondering about these issues throughout my latest experiment with continual glucose monitoring. The 4-phase framework aids clarify my pondering. It is apparent that CGMs give great descriptive data and with some exertion, I think you can also get some great diagnostic insights. But the sales pitch, as you’d anticipate, is explicitly concentrated on predictive and prescriptive guarantees: guiding you on what and when to try to eat in order to maximize efficiency and recovery. Perhaps that’s possible, but I’m not nevertheless convinced.

In simple fact, if there is one particular very simple information I just take away from this paper, it is that description and prognosis are not the exact matter as prediction and prescription. The latter doesn’t comply with mechanically from the previous. As the data sets continue to keep having larger and increased-excellent, it appears to be unavoidable that we’ll sooner or later get to the position when equipment-understanding algorithms can decide up styles and interactions that even hugely knowledgeable coaches might pass up. But that’s a major leap, and data on its own—even “big” data—won’t get us there.


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