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Drawing on the Data Provided by the Human Body to Maximize Performance
Endurance sports are defined as sports that involve sustained physical activity over a long period. The most common examples that come to mind are long-distance running, cycling, cross country skiing, hiking, and the combined swimming, running, and biking of a long-course triathlon. The main differentiator in endurance sports from strength sports is the lower average intensity during the activity. This is intrinsic to the duration and distances of the sports themselves – you can probably sprint 100 meters at maximum exertion, but you can’t sustain that same pace over the 26.2 miles of a marathon!
As is the case in every sport, how well you do in endurance sports depends on many different factors. A few examples include: how strong you are, what kind of shoes you’re wearing, the quality of your technique, etc. However, unlike other sports, endurance sports apply those factors over very long distances and times. When stretched out over a long distance or duration, the factors that affect performance are multiplied and magnified. If we go back to our sprint vs. marathon example – if your running shoes are too small for your feet, you can probably still sprint for 100 meters; try running in those shoes over 26.2 miles, though, and it will be an entirely different story.
Therefore, success in endurance sports is driven by being able to tightly control for and then optimize all of the factors, large or small, that could improve or detract from your performance over time. Take cycling as another example: some significant factors in cycling are the weight of your bike frame and choices in what gear to wear. In contrast, small factors could be things like the aerodynamic gains from shaving your legs before a race and the viscosity of the bike lube you apply to your bike chain. As David Brailsford (former performance director of British Cycling) declared in his theory called The Aggregation of Marginal Gains: “small improvements, when added together and applied over time, lead to significant increases.“
With an ever-present need to understand, monitor, and then control the wide range of factors that could influence performance, the Internet of Things (IoT) is positioned perfectly to bring in a new era of endurance sports.
The Problem: Costly, Slow, and Disparate Data Collection Practices Impede Progress
For many endurance athletes, especially those just starting, training and performance are measured mainly by feeling (e.g., “how hard was that effort on a scale from 1-10?”). Over time, the basic technology has been developed to collect more data around measuring physical performance – with heart rate monitors being the most common – which has opened a whole new window into training insights.
However, up until recently, there hadn’t been major changes to the technology itself. Looking back, the science of improvement in endurance sport has been hampered by the processes of data measurement being:
- Expensive or Unavailable: The necessary hardware, equipment, and sensors to make key performance decisions were either too expensive or simply didn’t exist, making it difficult for even professional athletes to access them.
- Delayed: Users could only review their data and metrics after they were downloaded from the device and uploaded to a computer, making it difficult to get timely feedback and nearly impossible to make real-time decisions.
- Inactionable: Data from multiple sources and sensors were presented to the user each in their own app, files, or software silos, without any synthesis or recommendations. With so many factors that could affect performance and no place to aggregate them all, many athletes were left to attempt improvement via trial-and-error, or were overwhelmed by what to action to take next.
IoT is relatively nascent in the endurance sports industry. Limitations on battery life, connectivity, and wearability have made it difficult to effectively harness the full benefits of IoT as it relates to endurance sports.
The Solution: Fully Connected, Long-Lasting Sensors Deliver Insightful Data
If you’re into running, you may already use an Apple Watch or Garmin to track your route with GPS, monitor your heart rate over ANT+, and stream music to your headphones using Bluetooth.
Recent advances and the maturity of IoT across both enterprise and consumer spaces mean that athletes have access to the necessary technology to make game-changing decisions and elevate their performance for the first time.
With IoT advancements, endurance sports have become:
- Fully connected with a suite of sensors: Improved battery technology, more accurate sensors, and greater portability means that a wide range of sensors are available to monitor previously unmonitorable things. Examples include Velocomp’s AeroPod to measure aerodynamic drag, and Stryd’s power meter to measure running power.
- Real-time: Wireless, power-efficient connectivity means that data from sensors are able to reliably and steadily transmit data to athletes in real-time. This means that athletes can view current data and make decisions that impact their performance mid-race, which can in turn influence whether a race is won or lost. An example is Supersapiens’ blood glucose monitor that performs real-time blood glucose measurmeents and reports it on a phone or Garmin watch to the athlete.
- Actionable: IoT platforms and software applications aggregate data from multiple sensors and sources into a centralized location; the platform can then synthesize that data to create charts, graphs, and other visualizations for viewing, identify performance trends, and make recommendations for future training sessions, recovery time, and next steps.
Looking Ahead: Portability and Maximum Battery Life are Top Priorities
The type of IoT sensors and hardware used in endurance sports run the gamut from basic heart rate straps to flashy, ruggedized GPS watches. Two things they all share in common are wireless connectivity and maximized battery life. You don’t want to deal with wires impeding your movement or to be forced to plug in to charge while you’re out exercising for hours.
For connectivity, most sensors do not connect directly to the cloud to maximize battery life – they typically transmit to a phone, watch, or cycling head unit via Bluetooth or ANT+. The nearby telephone, watch, or head unit may connect to the cloud via cellular LTE to send that data to an online platform but does so asynchronously from the sensor’s data collection. This makes the data for the sensors immediately available for viewing without a significant battery expenditure.
Once that data is available on the cloud, IoT platforms like Leverege or sport-specific software like TrainingPeaks will store and aggregate that data, analyze it, and present it in several helpful views. Users can interact with the software after their workout or race to understand how they performed and get data-driven recommendations to improve.
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