On Saturday, July 13th, Enola Labs’ CTO, Marcus Turner, presented a talk entitled “The Human App Instrument” at Google’s TX Devfest in Austin. The presentation touched on several aspects of the self-tracking movement known as quantified self and set his vision for the future of the industry.
The Human App Instrument
The quantified self-economy features several activity-monitoring devices as well as hundreds of applications that can track several aspects of your life. Turner believes that the problem with these applications is that they are disparate. They function and churn data for an individual independent of the other facets of human existence. Sure, an app can tell you how long you slept last night and can even illustrate your sleep cycle in an impressive visualization and analytical interface—but can it extrapolate that information to tell you how that data will affect your mood, efficiency, and behavior throughout the day?
On the hardware side of self-tracking, several options exist. One of the most popular, Jawbone UP, is a wristband that tracks hours slept, light vs. deep sleep, waking moments, distance traveled, calories burned, active time, activity intensity, and nutritional intake (complete with a nutritional database and barcode scanner).
While Jawbone UP and others like it are great at personal measurement, having the ability to aggregate the information in a single location and analyze the data to gain a better understanding of yourself and others is a critical aspect of the self-tracking movement. Several applications that attempt to aggregate your personal metrics are beginning to emerge. A great example is a collaboration between Qualcomm and WebMD in their creation of 2nethub. 2nethub basically allows the user to wirelessly sync their biometric data to a “system designed for security and interoperability.” The point of this tool is to allow consumers and physicians alike to pick solutions that suit healthcare needs. While applications like 2nethub are pushing the self-tracking aggregation market in the right direction, we are behind on our ability to collect robust data in a centralized location and being able to make meaningful correlations of the data in an attempt to improve individual wellness and widespread quality of life.
Using Big Data to Solve Big Problems
The highlight of Turner’s speech was his vision for the future of the quantified self-movement. Turner focused on several companies and projects that are using large sets of data to solve large-scale health issues.
US Flu Activity and Google
The first case study was Google’s work on discovering flu trends. Google was able to find that a close relationship exists between individuals searching flu-related queries on Google and actual flu contraction numbers. Why is this helpful? Think of it this way: if Google is able to understand exactly where virus’ are moving, geographically speaking, these areas will be able to take preventable measures, like vaccine stocking, in an attempt to stop the virus in its tracks. On a larger scale, the flu data can be used to help health professionals and public health officials better understand and respond to possible future outbreaks.
Patients Like Me
Patients like me is a website dedicated to connecting individuals who have similar diseases and conditions. Users are able to share their experiences, symptoms, drug trials, mood and overall well being with others in an attempt to generate ideas and data on the real world nature of diseases. This data can then be used on the individual level as well as for research by regulators, health care providers, and pharmaceutical companies who then are able to provide better care and services to these groups.
How do we use the data?
Turner mentioned several other companies that are using data to solve large-scale health issues. The overall theme of his vision is: organizations are finding interesting ways to gather data and use that data to make predictions. As soon as we are able to find an interface to correlate quantified self-data, can we use that data in an effort to solve large-scale health issues? Just as Google was able to predict flu trends using aggregated search queries, we can use aggregated health information to make certain predictions that can improve individual and certain demographic’s quality of life, reduce healthcare expenditures by understanding exactly where funds need to be allocated, and aid health professionals in their effort to detect, prevent and remediate any potential large-scale health issue.