An Open Source Solution for the Quantified Self Trifecta

The last five years have been redefining for the quantified self movement. Gone are the days that self monitoring was limited to pedometers and food diaries. The public has demonstrated a commercially viable interest in measuring steps, calories, sleep patterns, and even heartbeats. Over the years, this consumer-driven demand has led to a groundswell of technological advancements. The advancements in quantified self technology seem to be converging on a trifecta of quantified health: activity, vitals, and calories.

Activity

Quantified health emerged as a trend when digital pedometers evolved into full-blown activity trackers. Users went from being able to simply track steps to being able to track and differentiate between steps walked, run, bicycled, or climbed. Technology advanced again when the ability to track sleep patterns was introduced. Today, most standard model activity trackers offer a complete picture of our sleeping and waking hours and the energy we exert.

Vitals

By adding sensors to activity trackers, devices have started capturing heart rate, respiration rate, temperature, heat loss, perspiration, and more. Wireless scales are contributing daily weight and BMI to our increasingly complex digital self image. There are now even smartphone-connected devices capable of capturing clinical quality ECGs. The data is disparate, but growing in sophistication.

Calories

Many have argued that we are approaching the last point in history where weight is the only quantified data element that adults accurately track. But to move beyond that reality to a more sophisticated state state of self measurement, there needs to be a central platform that ties it all together and makes it all as easy to measure and understand as standing on a scale and coming to terms with the number it shows us. There is no single platform, app, or dashboard that can accomplish this now.

What holds this vision back is calories.  Science has simply not kept up in this space. We have calorie-counting smartphone apps that make the process of keeping a calorie diary a quite a bit easier by connecting us with calorie databases. However, these databases are limited to raw ingredients, such a one medium apple or processed foods that already have nutrition labels. Calorie counter apps do little when you are trying to figure out how to atone for eating that second piece of your grandmother’s lasagna. Compared to activity tracking, calorie counting is still largely a manual and error-prone process.

For calories to catch up, two important steps need to be taken. First, a more robust food database needs to be available for developers to reference, something that supersedes the current databases limited to nutrition label data. To that end, food activist Fred Trotter announced this week that he and his team have launched a crowdfunding campaign for creating an open source food database that, in partnership with Cincinnati Children’s Hospital, would build an open source, consumer-generated database of food. The database will contain the basic caloric and nutritional information currently available, but will also include “data about protein structures and relationships between ingredients.” The goal of the project would be to create a database capable of bringing nutritional research and app development to the next level.

Second, the act of logging food eaten needs to be as effortless as monitoring steps taken with a pedometer. This is an often referenced shortcoming in the digital health space, and while the work being done now is interesting, it is still preliminary.  Researchers at the University of Taiwan earlier this year tested a new device, installed inside an artificial tooth, that 94 percent of the time was able to accurately monitor the frequency users eat, drink, brush their teeth, cough, and even smoke. The prototype is not capable of breaking down caloric intake, but researchers describe it as a first-generation working concept designed only to substantiate the value of oral-based devices for capturing intake data.

Additional research is being led by Michael McAlpine, a professor of mechanical engineering at Princeton. McAlpine oversees a lab focused on scaling down material properties in order to create devices that are able to bio-interface with the body. His lab has created a “tooth tattoo” sensor out of a single layer of carbon sheets that is capable of detecting a variety of harmful bacteria as it enters the body. The technology could be used to monitor oral intake to alert users when harmful bacteria like E. coli or Salmonella have been consumed so that treatment could be sought before a full-blown infection sets in. This device is also not capable of monitoring caloric intake, but it adds weight to the concept of oral-based sensors and the contribution that later generations could make to our overall digital self-image.

Conclusion

Someday, we might all have a single tracker that displays calories consumed; activity undertaken and the resulting calories burned; sleep patterns achieved; weight; BMI; and trended vitals — a platform of real-time streaming health data. Other than calories, science isn’t too far from being able to deliver on this. Activity monitoring is the closest, though some users question the accuracy of commercially available activity trackers. Vital sign monitoring is becoming more commonplace in commercial devices, and the science behind accurately capturing that data is solid and established. Weight, BMI, and sleep pattern monitoring are all also commercially available — the data is just disparate and lives in silos.

If researchers working on calorie counting can catch up, a full picture of daily health could be achieved: intake, activity, and resulting changes to weight and BMI. The days of adults tracking only their weight might be replaced by a dashboard that presents a stark and honest picture of the cause and effect their lifestyle changes are having. The implications to quantified health, remote patient monitoring, population health, and chronic disease management are significant enough that if all the data could be captured and pulled together, the funding would be there to roll it out nationally.


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  • Lynn Vogel

    While applauding the technology developments in these areas, the real challenge is with self monitoring of markers for persons with chronic diseases, like diabetes. And this isn’t just a technolgy challenge, but one of significant behavioral change. Studies have shown a real reluctance among diabetics, for example, to self monitor on a consistent basis and then to make appropriate lifestyle adjustments. My guess is that there would be a similar reluctance among other similar groups as well.

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