Tag Archives: Twitter

Who Publicly Tweets Body Weight Using Withings?

On of the most well known QS devices is the Withings WiFi body scale. Automatically transmitting weight to a computer or mobile phone, the scale is a good example of a solid, mainstream approach to self-tracking. But I was curious recently to see how many people are taking advantage of the ability to publicly tweet their body weight. The number is not very large; normally just a few every hour, a few hundred in total, assuming most people measure their weight once per day. Certainly a small fraction of Withings users.  English tweets dominate, but German and Japanese tweets are also common.

QS friends know that I’ve been ranting lately against the notion that the only reason people will do anything is because they want to improve their visibility in a social network. The viral spread of Facebook, Twitter, and Foursquare have convinced some otherwise smart people I know that nobody will do anything that is not “social.”

This means that one of the most important features of a wireless scale is easy, automatic public tweeting of your weight. It makes a good story, but as far as I can tell it is not very real,  even among early adopters. I bother to point this out because the focus on public exposure of personal data obscures some of the reasons people actually do want to track themselves using convenient tools that stream data to computers and phones. They want this information not to share publicly, but for themselves, and perhaps for a small number of others who comprise their private network of close support.

Personal data has tremendous personal value. In aggregate, and anonymized, it is important for science and public health. But the theory that personal data, outside of sports and gaming, is a type of social currency is still waiting for some evidence to back it up.

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Health Hashtags: A Microsyntax for People and Machines

With the explosion of microblogging, tweeting, and status updates, it is clear that embedding personal metrics in social tools is on the tips of our fingers and is a natural extension to the personal toolbox. This post explores the opportunity of OHME (Open Mobile Health Exchange), a first-mover in the new world of Microsyntax, and a new entry into the microsyntax.org working group.

How it works
Taking Twitter as the backdrop, and the #hash being the first example, Microsyntax might be termed ‘in-line metadata’.  It is a self enclosed tag that associates this post with other like tagged posts.   It helps search, and it helps set context and find-ability.   
The first version of OHME adds more meaning to a set of personal metrics, including blood pressure, weight, steps per day, pain, and about 20 metrics a person can log using SMS, Twitter, devices, or nearly any tool that sends messages.  The project offers royalty free libraries for schemas and parsers.

Where it fits in today (person to machine)
When micro-blogging, or posting personal status, hashes can be used to help systems (machine readable) tools use these tags and syntax to facilitate actions.   For example, posting #spd=13045 suggests that a person has walked the equivalent of 13,045 steps in this day.   

With microsyntax there is a new dialog on how to aggregate device manufacturers, software vendors, and users to grow a vocabulary that thrives and rewards them with good tools and increased connection to their community.


Where it leads in future (machine to machine)
The advent of microsyntax, and OHME provides a new rhythm to the stream.   Mashups made from diverse streams of personal data allow new contexts to emerge, and new possibilities for action and specialization.  How will the health care system respond?  Will it become more patient centric, or merely use data generated automatically by various devices to make us more “hospital ready?”  Microsyntax such as the OHME project highlight the opportunity for every person to have quality streams of personal metrics.   Health loggers are already using microsyntax today.  Now is the time to build tools that aggregate and share these streams in meaningful ways.

Some considerations (machine to person is person to person)
In observing the landscape, it looks promising that natural alliances can form around syntax and vocabularies, giving rise to tools that support each other’s streams and have graceful hand-off from system to system.   In this new world model of data stewardship, a future can be seen where the microsyntax stream becomes more a critical resource.   It is in this context that enterprise class systems may emerge to help guide microsyntax systems towards reliable services.    

Today, our social web may be a bit fragile for such un-fettered live results about personal metrics.   A community designed sandbox for moving services gradually into the consciousness and letting first-adopters set the terms is a promise for microsyntax.   

Even though it is easy to type #911 #Robbery, our social and operational systems may not be as easy to accept the consequences of the message until we set rules and contexts of reliability – and the sender is authenticated in a way that grows

In this arena, microsyntax has both the honor of being extremely easy for the user (can do it without a mouse or selection) and to locate (parsers and search ala Twitter).   It also on the cutting edge of personal utility and personal safety and asks the question of how do we communicate personal streams.   

Speaking for one logger, this is a great step forward, the start of an the ecosystem that supports people and patients everywhere.   #OM+1!

Disclosure:  Mike Kirkwood‘s first post on Quantified Self, he is CEO of Polka a personal health platform. twitterhashtagblack.jpg
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“But my job description just said ‘office chair…’”

FlatulenceMonitor.pngAs John Herrman writes: Man Builds Chair that Tweets His Farts, Single-Handedly Justifies Twitter’s Existence.

Today’s New York Times has a story about twitter that, in passing, points out that SMS offers a pretty convenient format for automatic monitoring human behavior.

But I think the automatic flatulence detecting office chair tells the same story, more vividly. Instructions one can be found at instructables.com.

(Thanks to the comment from tilde.)

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Self-Tracking Through SMS

Just a quick follow up to the last post about Tweet What You Eat, inspired again by Flowing Data and by a telling anecdote from a recent health conference, where I concluded that ubiquitous self-tracking is coming, but perhaps not from the direction expected by many health professionals.

At the conference I met the CEO of health informatics company who had a seemingly clever idea about how to collect patient data, track compliance with medical recommendations (prescriptions, glucose monitoring, etc.), and provide reminders to patients and reports to their doctors. His name is Kent Dicks, and the company is called MedApps. Dicks is smart and well informed, and is working hard to make ubiquitous tracking work.

MedApps.jpgMedApps uses early generation wireless, where the connectivity costs are cheap and the bandwidth meager. The business idea was the create a general device, basically a wireless transmitter, that would transfer data over the cellular networks, and then arm this devices with a bunch of different customized dongles to connect to all different types of measurement tools: scale, glucose monitor, pedometer, cardiac monitor, etc. Owning a general, proprietary solution to an admittedly messy problem justifies a multi-hundred dollar price tag. After all, this is meant to be a medical system, approved by the FDA and paid for by the great mother of health care business plans: insurance reimbursements. Patients would comply with self-tracking because they wanted to cooperate with the regimen given to them by their doctors. If added motivation was needed, they could be influenced by insurance discounts, or even paycheck bonuses or penalties in the context of corporate “wellness” programs.

I long for a general solution to the data collection problem, but this approach strikes me as somewhat  wrong headed. It is serving the labyrinthine business system, rather than the more straightforward and obvious needs of people. (People who are not necessarily “patients.”) Meanwhile, companies like Zume Life are just having people read their data into a digital recorder and transcribing the reports, skilled users like Nathan Yau are writing Twitter bots, and small scale entrepreneurs like Alex Rossi are creating Web based services like Tweet What You Eat that collect reports via SMS. Data is beginning to flow through and around the highly disorganized and loosely connected networks that already exist, and as this flow increases I wonder if the more fully engineered and FDA approved systems will simply become irrelevant. And the bigger theme is also relevant. Either self-tracking will be understood as a good thing, and many people will want to do it, or it will not be so understood, and all the inducements in the world will probably not be enough to motivate them.

To get a sense of how low the threshold is for programmers to build a
simple system to gather personal data, take a look at Nathan’s recent
post on Flowing Data about how to make your own Twitter bot. Also read
the comments. When I did, I thought: “Pioneering users can make popular programs… This problem is on the way to being solved… ”

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Alex Rossi Shows Tweet What You Eat

This post makes me happy! One of the most fun things about QS so far has been the sense of optimism and possibility emanating from the frontiers of self-tracking. There is something so obvious about applying basic methods of rational data gathering and analysis to daily life that each little experiment, however simple, hints at bigger themes.

At the last QS Show&Tell, Alex Rossi showed his Twitter apps Tweet What You Eat and Tweet What You Spend. Since then, Nathan Yau of Flowing Data has posted about this attempt to track his eating through Twitter. Nathan wrote a little bot to collect Twitter messages about what he’s eating and how much he weighs and stick them in a database, which he can then use to chart his progress. He asked on his blog whether, if he made this public, people would be interested in using it.

Alex Rossi’s experience suggests that, yes, people would be interested. Tweet What You Eat and Tweet What You Spend are free apps Rossi wrote that do similar work: take SMS messages and post them to a database. Rossi has added some good tricks, such as crowd-sourcing the calorie count, so that suggested values are quickly available. But what I enjoyed most of Alex’s presentation was how clearly he outlined the power of this simple tool. I had just come from the Health 2.0 conference, where there was discussion of all kinds of complex mechanisms for gathering and presenting patient data. Devices, networks, payment systems, regulations – who was going to solve the puzzle? And then down to the QS Show&Tell, where one intelligent person, using a pared down protocol and an extremely simple social networking platform, hinted at a solution that is just around the corner, and that can’t be seen from the perspective of “health care.”

Anyway, here’s the video. My favorite quote: “I noticed people would debit exercise from their food diary. I was like, I didn’t even know I supported negative values!”

QS_081023_06_Alex_Rossi from Paul Lundahl on Vimeo.

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