Tag Archives: Self-Tracking
In the fall of 2011 we hosted our first European Quantified Self Conference. It was a fantastic time and we came away with new ideas, and the pleasure of bringing together a great group of individuals interested in self-tracking and self-knowledge. We see a lot of relationships form and blossom as a result of the bringing like-minded people together for few days of intimate sharing and conversation. With our the 2013 Quantified Self Global Conference on the horizon we wanted to highlight one of those relationships.
Sara Riggare is a QS meetup organizer (Stockholm), PhD student, and Parkinson’s patient. At the 2011 QS Europe conference she met Caspar Addyman, a psychologist and researcher. Together they’ve partnered on a few projects to create self-tracking tools for the Parkinson’s community. Watch their Ignite presentation at the 2013 QS Europe Conference to learn more:
Make sure to register for our 2013 Quantified Self Global Conference. We hope to see you there!
(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.
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.
We were fascinated by the conversation started Monday by the release of the Pew survey about self-tracking by Susannah Fox. As with any survey research, the top line results provoked the most discussion, and also some intelligent skepticism. We’ve had a few days to digest the results, and here’s our analysis of the two key questions:
1. How many Americans use technology for self-tracking?
2. Is this number growing, shrinking, or staying the same?
The always reliable Brian Dolan and MobiHealthNews pointed out that according to Pew, health-tracking numbers were unchanged since their last report in 2010. While the questions asked are not identical, it’s logical to conclude from the two surveys that the numbers are flat. Susannah Fox, who wrote the Pew report, states this clearly: “One in five trackers in the general population (21%) say they use some form of technology to track their health data, which matches our 2010 finding.”1
But wait; if this is true it is unexpected and therefore important. Continue reading
Today the Pew Research Center’s Internet & American Life Project released their latest findings in their ongoing research on the role of the Internet and technology in health and wellness. This latest report, Tracking for Health, is of particular interest to the Quantified Self community because it focuses on self-tracking. Thanks to Pew Associate Director, Susannah Fox, who gave us an advanced look at the results, we are able to bring you some reflections on this initial foray into measuring the impact of self-tracking.
Before we get to our discussion with Susannah it’s probably best to help set the stage with some of the most interesting findings.
Overview of Tracking
- 69% of adults track a health indicator for themselves or others.
- 34% of individuals who track use non-technological methods such as notebooks or journals.
- 21% of individuals who track use at least one form of technology such as apps or devices.
This feels very fast. A year ago there were a small number of Quantified Self devices, and a sense of high geekery. I walked into the Apple Store in Santa Monica last Wednesday and this is what I saw. There isn’t much that’s more mainstream than Apple retail at the moment, and I counted twenty-two Quantified Self trackers for sale. (Two were baby trackers and one dog tracker, borderline cases, but our curiosity extends to these.)
A question for readers: What kinds of self-tracking tools aren’t here now that will be here when I take this photo next year?
Here is a key to the photo:
- Pocketfinder personal GPS locator
- Tagg GPS dog tracker
- Fitbit One and Zip physical activity sensors
- iPING personal putting coach and app
- Wahoo Fitness bluetooth heart rate strap
- Scosche Rhythm heart rate monitor armband
- Jawbone Up physical activity and sleep sensor
- Pear Training heart rate monitor and training app
- Adidas MiCoach bluetooth heart rate monitor
- Adidas MiCoach Speed Cell activity sensor
- Nike+ sports sensor
- Nike+ Fuelband physical activity sensor
- Withings baby monitor
- Philips in.Sight wireless baby monitor
- IZON Wireless Camera -
- Philips in.Sight wireless camera
- Lark sleep sensor wristband
- Lark Life physical activity and sleep sensor
- iBGStar blood glucose sensor
- iHealth wireless blood pressure wrist monitor
- Withings blood pressure monitor
- Withings wireless scale
Last week we brought you a look into some of the interesting Quantified Self tools that were debuted at CES. Here are a few more we noticed from the deluge of CES coverage. Thanks to MobiHealthNews, Gizmodo, Engadget and many QS friends for the tips.
Withings Smart Body Analyzer (WS-50)
The latest wireless scale from Withings adds some interesting new sensors: resting heart rate, ambient air quality (CO2) and room temperature. The combination of physiological and environmental monitoring, while simple in this case, opens many new possibilities for Quantified Self projects.
Measures: Weight, BMI, Fat Mass, Heart Rate, Room Temperature, Room CO2
The Zensorium Tinke is a small sensor and companion app for iOS devices dedicated to helping users understand their health and wellness. This is a really interesting variation on the emerging theme of Heart Rate and Heart Rate Variability self-monitoring. The Tinke has no battery and no screen. Instead, the small optical sensor plugs directly into the iPhone.
Measures: Heart Rate, Heart Rate Variability, Blood Oxygen, Respiratory Rate
A similar approach is used by the Masimo iSpO2, where the focus is on blood oxygenation.
Measures: Blood Oxygenation, Heart Rate, Perfusion Index
The Mio Alpha boasts of continuous and strapless heart rate measurement. Using technology developed by Phillips, the Alpha uses optical heart rate sensing at the wrist and a soon to be released mobile app. What once seemed like difficult technical magic is on the verge of becoming commonplace.
The Mio Measures: Heart Rate
Sync: Bluetooth 4.0
Dale Lane is a software developer for IBM living and working in Hampshire and he has been developing neat personal tools for his self tracking for the last few years. Let’s take a look at a few of them.
Tracking TV Watching
Inspired by the background data collection offered by last.fm designed to capture music listening habits Dale set out to create his own “scrobbler” to better understand his TV viewing habits. What he came up with is amazing:
Using a bit of code running on his media PC he is able to track a number of variables including time of day, what program he’s watching, his most watched channels, and many many more. Take a bit of time to check out his comprehensive blog post about the project and the TV Scrobbling project page.
Not satisfied while merely understanding what he was watching on TV, Dale took it upon himself to better understand how we was reacting to what he was watching. Using a webcam and a bit more code he was able to piece together a program that snaps a picture and then uses the Face.com API to determine interesting characteristics about the picture. The Face.com API enables him to see if he’s smiling as well as estimating his mood based on the facial characteristics that show up in the webcam shot. This little program has enabled him to find out some really interesting things such as:
He was also able to track his estimated emotional state while gaming and found some interesting insights:
This shows my facial expressions while playing Modern Warfare 3 last night. Mostly “sad”, as I kept getting shot in the head. With occasional moments where something made me smile or laugh, presumably when something went well.
These are really interesting and unique methods for understanding ourselves and our behavior. Dale’s work on self-tracking is fascinating and is an inspiration to those of us looking to expand our understanding of ourselves and how we interact and react with the digital world. Be sure to check out his blog for more self-tracking projects and interesting tools!
Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.
This is the first time we’ve had video of a new meetup’s very first gathering! Ciaran Lyons started QS Singapore, and recorded his introductory remarks as well as his own self-tracking story. Great to watch for people new to QS or thinking of starting a meetup group. Thanks Ciaran!
In the last session of the day, we had a few experimental talks on noticing how food changes physical condition. It was also an interesting series of talks that shows the importance of collecting our own subjective data to back up or refute the other technological data that we might also have access to.
I kicked off the session with my talk “Quantifying My Genetics: Why I have been banned from caffeine”. My colleagues and friends helped me quantify my behavior after one, two, or three cups of coffee by giving my agitation a number from 0-10.
I found out that I’m a slow caffeine metabolizer from my genetic results and it seems like there is a correlation between how caffeine affects me and my genes. My genes are not deterministic, I couldn’t have known how caffeine affects me without making my own independent observations.
On a fun note, the crowd guessed that I had one cup of caffeine today, they were right, I had a cup of tea earlier down in the restaurant, away from the conference.
Next we had Martha Rotter who talked about how she experimented with her diet to solve her skin problems after doctors told her there was not much she could do. She did one allergy test where the results said she was allergic to chicken and soy- but after cutting out both of those foods, she did not see any changes but it gave her the idea to test different food groups.
After her experiment with a chicken and soy-less diet, she tried a few other food groups, eventually hitting on cutting out dairy. Her skin cleared up within two weeks of stopping drinking milk, eating cheese.
I think the take away message from our two sessions this afternoon, don’t be afraid to do your own testing, trust in your results.