Topic Archives: Discussions

Fun With Sparktweets

It’s no secret we love data here at Quantified Self, but we also love seeing how people interact with data. We’ve explored many of those interactions here and we’re always on the lookout for new and different ways people communicate their data and the insights therein.  A few weeks ago we wrote up a short “how to” post describing a recent phenomenon on Twitter – sparktweets. It didn’t take too long before we started seeing the Quantified Self community using these new “data words.”

We couldn’t stop thinking about sparktweets. What kind of data could you communicate in 140 characters? What would people do if it was easier to make a sparktweet? So we asked out friend Stan James to help us out and our Sparktweet Tool was born. Since then we’ve seen some great tweets roll though our feed, and we would love to see more. Need some inspiration? Here’s a few we really enjoyed:

What kind of conversation can you start with you data? Head on over to our Sparktweet Tool then make sure to add a link to your tweet in the comments or add to our conversation on the forum.

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Hands Free Heart Rate Tracking

A quick post here to highlight some interesting developments in the heart rate tracking space. Tracking and understanding heart rate has been a cornerstone of self-tracking since, well since someone put two fingers on their neck and decided to write down how many pulses they felt. We’ve come a long way from that point. If you’re like me tracking heart rate popped up on your radar when you started training for a sporting event like a marathon or long distance cycling. Like many who used the pioneering devices from Polar it felt a bit odd to strap that hard piece of plastic around my chest. After time, and seeing the benefits of tracking heart rate, it became part of my daily ritual. Yet, for all the great things heart rate monitoring can do for physical training, there have been very few advances to provide people with a noninvasive method. That is, until now.


Thearn, an enterprising Github user and developer, has released an open source tool that uses your webcam to detect your pulse. The Webcam Pulse Detector is a python application that uses a variety of tools such as OpenCV (an open source computer vision tool) to “find the location of the user’s face, then isolate the forehead region. Data is collected from this location over time to estimate the user’s heartbeat frequency. This is done by measuring average optical intensity in the forehead location, in the subimage’s green channel alone.” If you’re interested in the research that made this work possible check out the amazing work on Eulerian Video Magnification being conducted at MIT. Now, getting it to work is a bit of a hurdle, but it does appear to be working for those who have the technical expertise. If you get it working please let us know in the comments. Hopefully someone comes along that provides a bit of an easier installation solution for those of us who shy away from working in the terminal. Until then, there are actually quite a few mobile applications that use similar technology to detect and track heart rate:

Let us know if you’ve been tracking your heart rate and what you’ve found out. We would love to explore this space together.

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How To Make A Sparktweet

Update: Want to make your own Sparktweet? We made a simple tool that you can use. Check it out here!

I was stumbling around Twitter the other day when I was confronted with something new and different:

Apparently that little data representation is not all that new and different. Way back in 2010 Alex Kerin figured out that Twitter was accepting unicode and decide to play around and see if it could represent data. Lo and behold it could and a SparkTweet was born:

Before we get into how you too can start populating your Twitter feed and Facebook (I checked and it worked there as well) with representations of your own Quantified Self data let’s dive into some history.

The data visualization theorist and pioneer, Edward Tufte, is primarily responsible for the widespread use of sparklines. In his wonderful his book, Beautiful Evidence, Tufte describes sparklines as

a small intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphic can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic.

In another wonderful book, The Visual Display of Quantitative InformationTufte describes sparklines as “datawords: data-intense, design-simple, word-sized graphics.“ Of course, those of us in the QS community are deeply interested not only in data, but also in how data operates in society, what is means as a cultural artifact that is discussed and exchanged in language both written and verbal. This interest iswhat initially  piqued my curiosity.  The movement of data and a dataword distributed among text and publicly expressed in a tweet. I can’t help but wonder, what does this mean for how we think about and express data about our world?*

How To

Update:Thanks to our QS friend, Stan James, you can now make Sparktweets right here on Just head over to our Sparktweet Tool page and start making your own “data words.”

If you want display quantitative data in your Twitter stream it shouldn’t take you all that long to get started. Lucky for us Alex Kerin has provided a nifty little Excel workbook that will generate the unicode that can be pasted into your tweet. Just download this workbook and follow the simple instructions! Soon you’ll be able to send out tweets just like this:

For those of you with a bit more technical skill Zack Holman has made a very neat command line tool that will quickly generate the unicode for sparklines.

Now you’re ready and able to go forth and tweet your data! If you use a sparktweet to express your Quantified Self data be sure to let us know in the comments or tweet at us with #sparktweet and/or #quantifiedself.

*Of course the use of sparktweets is not without controversy in the world of data visualization. For more discussion on sparktweets and their utility I suggest you start here.


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Quantified Self as Soft Resistance


At the Quantified Self conference last year I attended a breakout session for scholars interested in QS as a research topic. There was an interesting range of fields represented, including medicine, anthropology, sociology, and public health. I’ve appreciated the criticism that the researchers bring. For instance, nearly all of the anthropologists are trained to see technologies of enumeration as tools of domination and control. That means that when they see us tracking ourselves, they wonder about whether the language of self-knowledge merely covers up for conformist obedience to corporate monitoring. They suspect we may be locking ourselves into our panoptical prisons. Their suspicions are sometimes soft pedaled out of politeness, but eventually they come out, and then the real conversation starts.

Recently, Dawn Nafus and Jamie Sherman, two anthropologists working at Intel Labs, have spent quite a lot of time talking with people they’ve met through the Quantified Self conferences and Meetups.  (Intel has been a Quantified Self sponsor and we are currently collaborating on a research project on how to improve the ways we can learn from our own data.) In the paper linked below, Nafus and Sherman discuss the relation between QS, narcissistic focus on individual improvement, consumerist gadget love, and what they call the “soft resistance” of real self-tracking practice.

This One Does Not Go Up To Eleven: The Quantified Self Movement as an Alternative Big Data Practice [PDF]

They’ve also described the direction of their work in this blog post.

The Quantified Self that we know has very little to do with trying to control other people’s body size or fetishizing technology. Indeed, people who use pen and paper are community leaders alongside professional data analysts.  As a social movement, QS maintains a big tent policy, such that the health care technology companies who do try to control other people’s body sizes also participate. But QS organizes its communities in ways that require people to participate as individuals with personal experiences, not as companies with a demo to sell.  This relentless focus on the self we suspect does have cultural roots in neoliberalism and the practices of responsibilization Giddens identified so long ago, but it also does important cultural work in the context of big data.


QSers self-track in an effort to re-assert dominion over their bodies by taking control of the data that many of us produce simply by being part of a digitally interconnected world.  When participants cycle through multiple devices, it is often not because they fetishize the technology, but because they have a more expansive, emergent notion of the self that does not settle easily into the assumptions built into any single measurement.  They do this using the technical tools available, but critically rather than blindly.  It is not radical to be sure, but a soft resistance, one that draws on and participates in the cultural resources available.

At the upcoming Quantified Self conference in Amsterdam on May 11/12, there will be another breakout session specifically for researchers who take QS as their topic, and I hope the critical dialog will continue.

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The New York QS Meetup Survey

Today’s post comes to us from Andrew Paulus, a member of the New York QS Meetup group.

QS Meetups are a great time to see demos, hear interesting show & tells, and talk to a few people after. With hundreds of members just in the local New York group it is impossible to meet everyone and hear about why they started tracking, get their thoughts on QS, or find out what tools/apps are popular. Consequently I created a survey to answer these questions and sent it out to the NY group. We received 102 responses that helped to paint a picture of who we are as a community. Many of my assumptions were confirmed, but there were also a few surprises. Let’s dig into the data!

Demographics & Basics

Age distribution: mean = 36.2 years old, youngest = 23 years old, oldest = 74 years old
Gender distribution: 67% male / 33% female
Currently working in a QS related company or have created a QS tool: 30%
Members who have a chronic health issue: 70%

Reflecting on the heavy male slant in the respondents made me wonder if there has been enough attention given to how QS tools could specifically help women. In the entire survey I neglected to mention any QS tools that are female-specific (such as pregnancy or menstrual cycle trackers), which is surely an area where self-tracking might play a prominent role.

We started by asking how many people self-track and what prompted them to start tracking. Ninety respondents, 94% of the sample, were actively engaged in a self-tracking practice. When we asked why individuals started self-tracking we found that in our sample the majority were interested in “understanding myself” or just plain curious. Given the common connection between QS practices and health we found it very interesting that health ranked fourth, tied with “work in the field or would like to start a business in the field.”

QSTOOLS_NYCWhat Tools Are You Using?

The top 10 most used tools were: Mint, Personal GoogleDoc/spreadsheet, Other, Foursquare, 23andMe, Fitbit, Runkeeper, Zeo, MyFitnessPal, and Goodreads with the Nike+ Fuelband and Lift just missing the top 10. Clearly personal tracking solutions are still prominent QS tools. I was surprised that over 20% of respondents had taken part in genome indexing through 23andMe, I didn’t realize how prominent this was in the QS community. (Click the image to see the breakdown of tools.)

Data Sharing & Privacy

Privacy is still important. Only 49% of respondents share their data with anyone and only 27% of respondents said they were ‘vey open’ to sharing their data with others.

Share data with someone else: 51%
Share data with a spouse/partner: 39%
Share data with a health professional: 14%

QS Tool Design Considerations

Over 60% of respondents said that the ability to export the data was important. Many of the major tools currently on the market do not allow you to easily export your data and you have to wonder if consumer pressure will finally get them to open up this data to the users. This also brings up two other points: first, whether there will be a standardization of data metrics across different platforms, and second, will there be a dominant platform or many smaller platforms that allow you to aggregate your data and help you derive meaning from the large amounts of data collected with various tools. (Editors note: Please see Gary’s wonderful post about “The QS World I’d Like to Live In“)



Thanks Andrew for putting out that survey and sending us the results. If you’re interested in more data from this survey you can view the full results and Andrew’s notes here.

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The State of Self-Tracking

(Co-written with Gary Wolf)

In January we started asking ourselves, “How many people self-track?”  It was an interesting question that stemmed from our discussion with Susannah Fox about the recent Pew report on Tracking for Health. Here’s a quick recap of the discussion so far.

The astute Brian Dolan of MobiHealthNews suggested that the Pew data on self-tracking for health seems to show constant – not growing – participation. According to Pew, in 2012 only 11% of adults track their health using mobile apps, up from 9% in 2011.

All this in the context of a massive increase in smartphone use. Pew data shows smartphone ownership rising 20% just in the last year, and this shows no signs of slowing down. Those smartphones are not just super-connected tweeting machines. They pack a variety of powerful sensors and technologies that can be used for self-tracking apps. We notice a lot of people using these, but our sample is skewed toward techies and scientists.

What is really going on in the bigger world? How many people are actually tracking?

A few weeks ago ABI, a market research firm, released a report on Wearable Computing Devices. According to the report there will be an estimated 485 million wearable computing devices shipped by 2018. Josh Flood, the analyst behind this report indicated that they estimated that 61% of all devices in wearable market are fitness or activity trackers. “Sports and fitness will continue to be the largest in shipments,” he mentioned “but we’ll start to see growth in other areas such as watches, cameras, and glasses.”

One just needs to venture into their local electronics retailer to see that self-tracking devices are becoming more widespread. So why are our observations out of synch with the Pew numbers?

The answer may lie in the framing of the Pew questions as “self-tracking for health?” For instance:

On your cell phone, do you happen to have any software applications or “apps” that help you track or manage your health, or not?

Thinking about the health indicator you pay the most attention to, either for yourself or someone else (an adult you provide unpaid care for), how do you keep track of changes? Do you use paper, like a notebook or journal, a computer program, like a spreadsheet, a website or other online tool, an app or other tool on your phone or mobile device, a medical device, like a glucose meter, or do you keep track just in your head?

We think it is likely that many practices we include in our definition of Quantified Self are not being captured by the Pew Research. A person who tracks a daily run with a Garmin GPS watch might show up in the wearables data that ABI looks at, and might look to us as a self-tracker for health, but might be invisible to Pew. There may be even self-tracking practices that fall outside health or wearables. We’ve seen a large number of people who track time and productivity using computer applications such as RescueTime, apps that support well-being such as meditation trackers, mood trackers, and diet trackers; and apps that support general self-reflection and journaling, such as a life-logging app. Many self-tracking practices do not fit neatly into “health.” (Though they may influence health!)

In a way, there is a parallel here to what we found when we compared Fitbit with Fuelband data. Both of them produced different numbers for “steps.” When we got into the details, we ended up thinking that this was not a matter of one being closer to the “ground truth,” but of intentionally different interpretations of messy accelerometer data. Fitbit gives more step credit for general movement, because it is a lifestyle/activity tracker; Nike might prefer to credit intentional exercise, since the Nike brand sits closer to sports. Context matters.

This confusion about what is health tracking, what fits in the frame, is closely analogous to many other confusions in the conversation about health generally. It is common now in the healthcare world to talk about how the larger determinants of public health are outside the control of the healthcare industry; for instance, diet, exercise, stress, and exposure to environmental toxins. Sometimes people who make these observations follow them with a call for the healthcare industry to begin addressing these larger concerns; for instance, to “medicalize” tracking apps by making them prescribable and reimbursable by health insurers.

But maybe this isn’t the only approach. If the “healthcare” frame isn’t adequate to capture the most important determinants of health, we could try switching frames. What our journey through the self-tracking data suggests is that the opposite approach might be useful to consider: start with the bigger world of self-care practices, and enhance these. Why? Because that’s where we trackers already are. That is, how are we deriving meaning from self-tracking? That’s the mental framework that we typically use, and that we like to use. That’s where the growth – in terms both of us, and of cultural understanding, engagement, and knowledge-making – might really be happening.

We don’t know this for sure. We take the Pew data as evidence that this approach is worth trying.

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The QS World I Would Like to Live In

In the QS world I’d like to live in, our personal data would be easily available to us to learn from using many different methods and tools. Here are some conditions I think would make this easier:

  1. Data can be exported from the various systems we use into a simple format for exploration.
  2. We can store and backup our data using whatever method we want.
  3. We can share our data with whomever we want.
  4. We can rescind permission to look at our data.
  5. We can flow our data into diverse visualization templates and analytical systems.

I’ve tried to express these conditions briefly and simply, but any of them – and certainly all of them together – require changes in the systems we currently use, and these changes may be challenging for technical, business, social, and political reasons.

I know many people in our community have worked on parts of this problem, and I’m interested in your comments and ideas.

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Nike+ FuelBand vs Fitbit Tracking: Spring 2013 Update

Here at QS Labs we’ve been curious about the differences between two of the most popular devices among self-trackers: The Nike+ FuelBand and the FitBit. I’m the latest experimenter on this topic, and since January I’ve been wearing a FuelBand on my left wrist and a FitBit (original model) in the right hand coin pocket of my jeans. The FitBit almost always counts significantly more steps than the FuelBand.

The details are interesting. When Bastian compared his FuelBand vs his Fitbit, he found a slight correlation between his activity level and the difference in the number of steps they counted. In other words, them more active he was, the more the two devices disagreed. When Ernesto did his FuelBand vs Fitbit test, his numbers closely matched. My data is more like Bastian’s, but with the effect of high activity even clearer. Look at the graph below. On the vertical axis is the difference in step count, by day. On the horizontal access is the number of daily steps Fitbit counted. The higher the number of “FitBit steps,” the more likely it is that “Fuelband steps” are much lower.


Continue reading

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QS at Best Buy

A month ago we showed you what we thought was the quintessential example of how Quantified Self is becoming more of a mainstream activity. During a trip to the Apple store we identified over 20 different Quantified Self devices. Another outing led me into one of the largest consumer electronics stores in the US: Best Buy.

Here, I counted over 25 different tracking devices on the shelves. I’ve split them into three categories here so you can get a sense of just how many different devices are available. With a bit of internet sleuthing I also found that additional devices are available at different stores so you might see something different in your local Best Buy.


General Health

  1. iHealth Wireless Blood Pressure Wrist Monitor
  2. iHealth Wireless BloodPressure Cuff
  3. iHealth Bluetooth Body Analysis Scale.
  4. iHealth Bluetooth Scale
  5. iHealth Blood Pressure Dock
  6. Fitbit Aria Wireless Scale
  7. Withings Wireless Scale
  8. Withings Blood Pressure Monitor Continue reading
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Larry Smarr: Where There Is Data There Is Hope

“I never thought I would be getting into this business.”

This is the first sentence in a mind-expanding talk by Larry Smarr about his self-tracking journey.

In 1999 Larry moved from Illinois to La Jolla, CA to take a position at the University of California, San Diego (UCSD). Like most Southern California transplants he quickly adapted to the local norms and began looking for ways to improve his fitness and health. Continue reading

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