Digital fitness: Increasing training efficiency with big data

DXC Technology recently published 6 Technology Trends for 2018.  Number three on this list is the “Quantified Enterprise,” where “companies will harness the ‘data exhaust’ from digital systems to quantify the business and become even more productive.”

This trend also applies to using data to train more efficiently for a sport to get better results.  Let’s talk cycling and running.

As with many areas of business, in training (or actual racing) there are two types of variables, input variables and output variables. One of the most common input variables is heart rate (HR), as this is typically a good proxy for level of effort.  The most common output variables are power (in watts) on the bike and pace on the run.  Combining these variables gives us two efficiency metrics, Power:HR and Pace:HR.  As you train, one of the primary objectives is to increasing this efficiency over time such that for the same level of effort (HR) you are putting out more power or speed.

In endurance training, given the need to hold this efficiency for long periods of time, we also want to look at how these values drift over time as we increase the length of the race or training session.  Athletes who have trained over longer distances should maintain the same efficiency level throughout the session.  In less fit athletes, this efficiency value will drop as the session progresses, as the athlete needs more effort to maintain the same output.

Big data made easy

To do this analysis, the first step is to collect the data.  Thankfully, with IoT devices this is easy.  I use a Garmin 920xt GPS watch to sync with a heart rate monitor strap, and a power meter on the bike; I leverage GPS to gather run pace data.  The watch can then capture pace, power and heart rate in addition to other metrics like cadence at a rate of once per second.  This means over the course of an hour I have 3,600 data points with multiple metrics to drive insight.

Once the workout session is complete, the GPS watch will Bluetooth-sync with an app on my phone.  Once it syncs to the phone that data is set to automatically be shared with multiple online training platforms.  One of the platforms I have it set to sync to is Training Peaks, a SaaS online platform that will run analysis and provide visualizations. See an example below.

Increasing-Training-Efficiency-PWR-HR-Graph

Power and heart rate chart by time for a cycling training session

Training Peaks will also leverage map data and GPS coordinates from the workout to provide a Normalized Graded Pace that takes into account changes in elevation to provide a more comparable pace number to other workouts.

There is also a mobile app for viewing this information on the go, so after the workout I can instantly assess my performance.

Increasing-Training-Efficiency-App-View

Mobile app view of a recent run

 

If I can go for a run and capture 3,600 data points, wirelessly sync to a cloud application, and instantly have access to advanced insight on performance — all leveraging consumer technology — shouldn’t this type of data capture and analysis be standard in a Fortune 500 company to better assess and improve its own performance?


Nicole Zenel headshotNicole Zenel works for DXC Technology as the Director of Global Deal Governance, Front Door and the Sales Information Center.  In her spare time, she is an avid cyclist, triathlete and mountain climber.  She enjoys learning and writing about how the latest IoT, big data and other technologies can be applied to these and other fitness pursuits. @NC_Zenel

Comments

  1. Nice article….I’m looking forward to seeing predictive analytics in this space. i.e. if I execute workout A and intake meal B, and I get X amount of rest…my power, HR, etc….should be optimized by C for my next race. Very cool!

    Like

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