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The Coming Revolution in Activity Measurement
Wearable Tech World Feature Article
June 17, 2015
The Coming Revolution in Activity Measurement
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By Kerri McMaster
co-founder and chief strategy officer, Performance Lab

Why are all these heart icons on my phone?

Not content with its push into the automotive industry, Silicon Valley now appears primed to become a formidable force in healthcare. All of the tech titans seem to be developing platforms for collecting and aggregating personal health and fitness information. Apple (News - Alert) has even been running a “secret lab” to collect what senior executive Jeff Williams describes as “one of the world's largest pieces of data on fitness.”


The reason for this focus on activity data is that wearable devices are very suited to collecting such data. A key point is that the data can be gathered automatically. This means that users don’t need to spend time manually logging their activity, which often results in lower usage and reliability of data. The business goal is to correlate activity data with health outcomes, providing an evidence base from which to prompt the users of those wearable devices to change their behavior in positive ways. The potential benefits for health promotion are undeniable.

The Problem: Not all steps are created equal

One of the most significant problems with Silicon Valley’s healthcare project is the data collected lacks context. The data shows that my heart rate rose after I passed the 1,000 steps mark. But was this because I started sprinting? Because I started climbing a hill? Because I was ill? Or maybe because I swung my daughter onto my back when she started to grumble? Your wearable device doesn’t know. And because it doesn’t record this contextual metadata, drawing inferences from the data is dubious.

The Solution: Think multi-dimensional

We describe the current reports that wearable devices and apps provide as one-dimensional. That is, they basically report what they are measuring (heart rate, steps, altitude, distance travelled and so forth). A multi-dimensional report produces insights by considering multiple metrics simultaneously. For example, an “effort” metric (such as heart rate or power output) can be considered together with other metrics that shed light on what is actually going on. These include:

  • resistance metrics such as gradient, altitude change, or distance between limb turnover
  • a comparative effort metric, such as heart rate the last time the user encountered this type of resistance
  • real-time biomedical metrics, such as blood pressure or lactic acid

Only a multi-dimensional report can reveal what happened in meaningful terms, for instance “your legs got fatigued because your effort on the hills was too high.”

The good news is the same devices that are capturing non-contextualized data also have the potential to capture the extra dimensions required to make the former meaningful. Barometers are becoming standard on smart phones. Together with speed, stride length and stride rate we can use this altitude data to provide all kinds of context—and conveniently accelerometers, and the algorithms that interpret them, are now smart enough to compute precisely those metrics. Heart rate measurement is firmly established on wrist-based devices, which are now set to provide accurate 24/7 heart rate variability data.

Going to the Next Level: Real time coaching

The upshot of this coming revolution in activity measurement is that our wearable devices will record in meaningful terms what we did and how our bodies responded.

One benefit of this multi-dimensional approach is the project of correlating activity with health outcomes will no longer be dubious. Rather than wait for data analysis to provide us with generic health promotion advice, we can receive real time coaching.

If our wearables now understand what we are doing, they can also correct this behavior. We can determine, for a given individual, how his or her unique body works to maximize and improve outcome variables such as power output and fatigue levels. So for instance we can discover the optimum stride rate and stride length for a previously sedentary person looking to lose weight through walking, balancing their need for calorie burn with management of their pain and motivation levels.

My prediction is that health and fitness advice will be real time, and customized to individuals.




Edited by Dominick Sorrentino

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