For millennia people have run by feel, an «art of combining our breath and mind and muscles into fluid self-propulsion over wild terrain,» says Christopher McDougall in his anthropological study of the topic.
Many of us still run this way, of course, but for how much longer? Now we can lace up a pair of «smart» sneakers and instantly shift from running by feel to running by metrics. Guesses at how far and how fast are replaced by real time stats on pace and meters travelled.
If you think you’ll never make the switch, think again. As Nike learned from studying millions of users, the magic number of times a runner needs to see her data before becoming a more «science-based» runner is just five. Once a person crosses that threshold they are «massively more likely» to keep running by metrics than by feel alone.
That’s a great number. Here are five more I’ve come across in my ongoing study of the field of auto-analytics.
Auto analytics have a long tradition in the U.S. Benjamin Franklin was an early adopter, though his self-tracking experiments grabbed fewer headlines than his apocryphal kite-flying ones. Franklin quantified his progress toward achieving 13 personal goals, assigning himself a «little black spot» on days he failed to make progress on a particular goal.
New research suggests that 69% of Americans participate in some self-tracking behavior just in areas related to health and wellness. Within this group of self-trackers there’s a fundamental behavioral switch going on from analog tracking to digital. Old-school methods requiring you to painstakingly detail your life with pen and paper are being replaced by tech that can collect data automatically or passively, and even interpret the data for you. Already 21% of people who self-track use smartphone apps or gadgets that make self-tracking behavior more efficient and the data more dependable.
We take for granted now that, standing in a hotel lobby, we can find the quickest route to our destination, learn the name of the song playing and change a meeting time, all witha few taps. We navigate the external world this way. But have you thought much about using algorithms to discover the seemingly invisible and silent world within yourself, of cognition, physiological functioning, and emotions?
More of us will eventually do this. By 2018, 485,000,000 wearable computing devices will ship globally, including smart watches and smart clothing, according to ABI Research. And don’t suspect this just means we’ll all be wearing dorky electronic glasses. Sensors will detect everything from the number of steps we take to minutes of REM sleep per day. Many killer apps for wearable analytics probably haven’t been imagined yet.
People tend to gravitate toward the health and wellness applications of auto-analytics. But they will be used for «softer» disciplines too, like innovation and creativity. Traditionally measures of creativity, cognition, and focus have been a «mysterious art,» as Tom Davenport points out. But many types of wearable computing will allow professionals to migrate from art to science in the way they monitor their work and try to improve the thinking part of their job performance.
Consider that lab research using EEG headbands already shows users tend to have a measurable spike in gamma-band brain waves 0.3 seconds before the «aha!» moments that spark the creative process. Many of these headbands are coming out of labs and are now available on store shelves, allowing anyone (who’s willing to look a bit geeky) to measure their neurons firing and seek patterns in their creative thinking, and adjust your routines to enhance creativity.
When we think of Big Data, we tend to think of large organizations, even nations, crunching terabytes of information. But you have Big Data inside yourself. Consider the BodyMedia FIT Armband, which contains four sensors and collects 5,000 data points a minute on your metabolism, sleep patterns, and activity levels. That’s 2.4 million data points in a work day.
What’s more, this is the kind of big data that comes with analysis: The wearable technology uses code and insights from IBM analytics teams to crunch your data and offer algorithm-generated recommendations on how users might improve personal decisions on diet and exercise to optimize health.
We began by mentioning how the ancient art of running can now be made scientific. Now think broadly about what other once-immeasurable forms of human movement might benefit from quantification, from karate kicks to dance moves to top-spin forehands.
Apple’s recently amended 84-page patent filing shows the extent to which the company has been thinking about changing self-measurement the way it changed music. As one analyst sums up: «the company is…developing an entire wearable/detachable computing platform and ecosystem comprised of wireless sensing systems for monitoring…sports activity, athletic training, medicine, fitness and wellness in humans.»
Most intriguing is Apple’s interest in developing wearable devices that offer users quantitative insight on movement in business and industrial settings. Soon you might be measuring your attempts to «manage by walking around (MBWA)» or to use more hand gestures during key client presentations.
Frederick Taylor’s famous Time and Motion studies aimed to make factory work more scientific. Imagine if Taylor had the tools we’ll have now. It looks like we’re moving toward a New Taylorism, only this time, the worker takes control of measuring effectiveness. It could create increased autonomy through self-knowledge, and revolutionize, again, management, and the way they live and work.