Tag Archives: Self-Tracking
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.
A colleague I’ll call John has decided to start tracking his mood for a long period of time (years). He explains why:
A few years ago, after a severe manic attack, I was diagnosed with bipolar disorder. The attack was preceded by an intense period of stress, then two weeks of elevated mood, increased social activity (hanging out and meeting people), and racing thoughts (hypomania). Then I skipped a few nights of sleep, wandered down roads in the middle of the night, and eventually became psychotic, in that I could no longer distinguish between reality and imagination. I was chased by cops on several occasions, and was involuntarily committed to the mental health wing of a hospital for a month. It put a massive dent in my life.
Family, medicine, and time helped me recover. Being out of control like that was fun only for the first two weeks. Having my life turned upside down was not fun either. As I recovered I became increasingly interested in finding ways to prevent a relapse. One doctor said: You have a vulnerability. You need to protect yourself. I agreed.
Looking back on the experience, I realized there was a rise in odd behaviors two weeks before I started to skip nights of sleep and fell into psychosis. There was an even longer buildup of stress, anxiety, and fear in the months before the mania hit. During the last two weeks before the mania, my behavior was different from what is normal for me. I felt elated and had a sense of general “breakthrough”. I suddenly felt no fear and anxiety. I felt on top of the world. I was constantly taking notes because ideas and thoughts were running through my head. I scheduled meetings and social activities almost constantly throughout these two weeks and shared my experiences as my new self. As I started to sleep less and skip nights of sleep, others later told me I seemed agitated and down.
Maybe it is possible to catch these early warning signs and take counter measures before they worsen into mania or depression. This is why I have started to track my behavior starting with mood and sleep. If I can get a baseline of my behavior and know what is ‘normal’ for me, it will be easier to notice when I am outside my normal range. I can alert myself or be alerted by others around me who are monitoring me. Long-term records of mood will also help me experiment to see which things influence my mood. This may give me more control over my mood.
Mood tracking might be a good idea for anyone to do, but it may be especially helpful for people with a bipolar diagnosis. Everyone has mood variation. For bipolars, however, mood swings can be more extreme (in both directions, up and down) , have far worse consequences (psychosis on one end and suicide on the other), change more rapidly, and be more vulnerable to environmental triggers like stress. The good news is that the first changes in mood can happen hours or days before more extreme changes. This gives people a chance to take countermeasures to prevent more extreme states.
The project name refers to the fact that Van Gogh had bipolar disorder.
The report is called The Social Life of Health Information, and has several interesting findings. Here is an excerpt:
Carol Torgan, a health science strategist, points out that anyone who makes note of their blood pressure, weight, or menstrual cycle could be categorized as a “self-tracker.”10 Add an online component, and you have the ingredients for a social health application or an electronic health record. Our survey finds that 15% of internet users have tracked their weight, diet, or exercise routine online. In addition, 17% of internet users have tracked any other health indicators or symptoms online. Fully 27% of adult internet users say yes to either question.
Wireless users are more likely than other internet users to track health data online. Eighteen percent of wireless users have tracked their weight, diet, or exercise routine online, compared with 9% of internet users who do not have a wireless-enabled laptop or other device. Nineteen percent of wireless users have tracked any other health indicators or symptoms online, compared with 11% of non-wireless internet users.
Separately, looking just at the 85% of adults who own a cell phone, 9% say they have software applications or “apps” on their phones that help them track or manage their health.