How We're Measuring Ourselves to Better Health People immersed in the Quantified Self movement collect vast amounts of data about their health but analytics still needs make easier to apply.
By Stefan Groschupf Edited by Dan Bova
Opinions expressed by Entrepreneur contributors are their own.
If you can measure it, you can improve it. Thanks to the explosion in smart devices, consumers are measuring and monitoring all aspects of their lives, giving them new ways to take control and improve their overall well-being. Known as the Quantified Self movement, people are taking advantage of new technologies to track, analyze and optimize how they live and care for themselves.
But for this movement to truly take off on both the individual and community level, technology needs to mask the complexity of Big Data analytics so that it becomes simple and intuitive. It needs to seamlessly integrate into the daily routines and lives of health care providers and everyday people alike.
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Fitness and health devices, such as FitBit and Jawbone are the most popular type of health-related products among today's consumers, according to MIT Technology Review's Data-Driven Health Care report. With these devices, people are willingly capturing data about everything from sleep and heart rate to calorie intake and hydration, but a roadblock remains between capturing all of the data and actually analyzing it to impact daily life choices.
This is why I love the U.S. Women's Olympic cycling team story. It's a great example of what can be accomplished when you put data science into the hands of individuals and those with area expertise. Turning to quantified-self techniques, the team went from a 5-second deficit at the world championships to earning a silver medal in the 2012 London Olympics, a triumphant combination of athletic ability bolsterd by integrating, analyzing and visualizing self data.
The team correlated performance and health data to uncover patterns that could make or break their race. With little to no funding, they turned to intuitive technology and their knowledge of the sport and team, rather than a data scientist. Immediately, they learned how routines and behaviors could be adjusted to naturally maximize human performance. For example, data revealed how temperature exposure altered time spent in deep sleep, a state where the body naturally releases testosterone. To increase the team's amount of deep sleep, they adjusted temperatures accordingly so they were able to recover faster and perform better.
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By integrating, analyzing and visualizing quantified self data to find insights that improved training, the 2012 U.S. Women's Olympic cycling team propelled themselves to glory and earned the silver medal in London. Their approach not only revealed how data could optimize athletic performance, but also gave a glimpse at what can be achieved from simplifying data analytics.
Imagine replicating what the U.S. Women's Olympic cycling team at a personal and collective level. With insights into health data, people can implement individualized self-care tactics that help them sleep better, exercise efficiently, eat properly and ultimately be healthier. On the collective level, communities can design tailored health care systems, prevent disease and lower health care costs.
Simplified data analytics empowers individuals to take a more active role in their personal well-being. I'm looking forward to a not-so-distant future when we'll start to see the quantified self-movement move from individuals to communities to create a more informed and healthy population.
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