Tag Archives: Self-Tracking
Maria Benet began tracking her activity a few years ago as a way to lose weight and take control of her health. What started with a simple pedometer and a few custom Access databases has morphed into a multi-year tracking project that includes news apps and tools. Her progress and data has even spurred her on to new experiences and athletic endeavors. Watch her talk, filmed at the Bay Area QS meetup group, and read the transcript below.
(Editors Note: We’re excited to have Maria attending the 2014 Quantified Self Europe Conference where we hope to hear an updated version of this wonderful talk.)
What did I do?
Hi, my name is Maria Benet and I am happy to tell you that only about two-thirds of me is here to talk about my tracking project. I mean that literarily, because in the 10 years since I’ve been self-tracking I lost over 50 pounds while getting fitter.
In my early 50s, I was overweight, out of shape, with bad knees, and when not cranky, depressed. I was already on meds for high blood pressure and was looking at the prospect of more prescriptions down the road.
So, what did I do to change my situation? I set about tracking my activity levels, my weight and my food intake with the help of apps, wearable devices – plus — in databases and Excel spreadsheets that I designed. Until late 2011, I tracked inconsistently, but once I discovered mobile apps and wearable devices — I became more systematic and consistent about tracking weight, food intake, and fitness data.
How did I do it?
When I first started — losing 50 pounds seemed daunting, but going for a walk at least 5 days a week seemed less formidable. To track walks I was going to take in the hilly neighborhood where I live, I created a simple Access database.
I bought a pedometer, hiking shoes, and off I went. After walking, I recorded the duration, the number of steps, and calculated the distances I covered. I also charted my routes by naming the streets, and made notes about the weather and my mood during the walk.
Recording the data turned out to be a form of reward in itself. At the start of this tracking project, I enjoyed seeing the database grow a little more than I enjoyed the actual walks themselves.
Over time, the walks got longer, steeper, and eventually included actual hikes. I also took up the practice of Yoga regularly, and then added Pilates to my exercise repertoire.
Along the way, I also started to lose weight. Though I didn’t weigh myself every day, I began to pay attention to the kinds of foods I ate and tried to wean myself off processed foods in general.
They say you get fit in the gym, but lose weight in the kitchen. In September 2011, when I discovered LoseIt, it became my virtual kitchen: LoseIt helped me see what foods I ate regularly, which of these spiked my weight, even if my calorie intake stayed the same. I noticed these relationships anecdotally, rather than by finding statistical correlations between them.
Tracking in LoseIt helped me realize that as much as I love bread and beer, they are not my friends. Two years ago, an allergist confirmed my wheat sensitivity through blood tests and an elimination diet.
I added Endomondo to my tool box a few months later, since I liked having the maps and stats it offered, in addition to the other data it showed. By December I also added a Fitbit, as with it I could track more accurately how many steps I took and approximate better the number of calories I burned. The Fitbit was like going back to the pedometer, but to one on steroids.
With the Fitibit, I focus mostly on the Very Active Minutes it claims to measure. Increasing that number over time became a game. In 2012, I was averaging about 57 minutes a day, which put me in the 98th percentile. Increasing to 69 minutes only brought me to the 99th percentile, as the Fitbit population also has increased over time.
The Fitbit turned out to be a catalytic tool, because it spurred me on to push the perceived limits of my fitness abilities and possibilities further. It ended up putting wheels under my dreams.
In the spring of 2012,I took up cycling to increase my active minutes and challenge a mental habit of opting out of things because of a fear of failure or thinking of them as not age appropriate. Biking, in turn, added to my collection of gadgets and apps for tracking the metrics involved.
By 2012 then, in addition to LoseIt and Fitbit, I was tracking workouts with a Garmin GPS watch with a HR monitor and my bike rides with a Garmin Edge computer, uploading the data to the Garmin site, to Endomondo and Strava, as each had strengths the other lacked, from my perspective.
To complicate data gathering, back in January 2012, I started a basic Excel spreadsheet that tracks highlights from each of these apps in an application-independent reference for me. In Excel I track the type of activity, duration, distance, if applicable, average and maximum heart rate, Strava suffer points, (a measure of exertion), the hours I slept and how that sleep seemed to me, and additional notes about the day I might think relevant.
The plethora of my gadgets and apps might earn me an entry into the next edition of The Diagnostic and Statistical Manual of Mental Disorders. But exploring these tools was, and still is, my way of looking for a comprehensive and personalized way to track the quantities in my habits and activities that make for a qualitative difference in my life … which brings me to what I learned so far:
What did I learn?
I learned that small quantitative changes in particular daily habits add up to a big difference in quality of life in general.
The incremental additions in my tracking methods and number of gadgets I added produced a lot of data, which I haven’t analyzed closely, because I was already getting a lot of return from them in the form of new experiences in my life.
The most memorable of these experiences is my having completed the metric century ride on the Tour de Fuzz in Sonoma last September. In the space of a little over a year I went from covering barely 8 miles in an hour on my first rides to completing 63 miles in 5 and ½ hours and feeling ready to ride a lot more.
It has been said that motivation is what gets us up and going, but it’s habit that keeps us going. So it is with my tracking: though the motivation was to lose weight, the habit of tracking and keeping an eye on the numbers are what allowed me to go from daily small changes to a much bigger transformation from the overweight, depressed, and achy person I was 10 years ago to who I am now: someone interested in health and fitness and setting goals I can meet.
I learned that for me the act of tracking is the project itself. Although the data I generate can be charted and described in numerical relationships the number that brings me the information that makes a difference in my life, is a simple 1 – or tracking one day at a time.
I love to see the numbers my Garmin and Fitbit generate, but in the end, the quantified self for me is not so much about the measured life as it is about keeping those numbers coming through a well-lived and, more importantly, well-enjoyed life as I go from my fitter fifties into what I hope will be my sounder sixties.
How will children respond to a world where personal data is ubiquitous? Bill Schuller is starting to find out with his two young children and will be sharing his story at the upcoming 2013 Quantified Self Global Conference.
Bill started tracking his exercise and weight in 2010. His preschool-aged son, listening to his father talk about his daily metrics at the dinner table, began to imitate Bill’s tracking behavior, regularly stepping on the scale, not to watch his weight, but to “just check my numbers.” Bill then designed tracking games for him and his son. One of them involved putting things away in the house while tracking steps and gaining “clean-up points.”
This fun talk will feature more stories on the creative ways Bill and his children are playing with self-tracking. As a preview, we have a version of the talk that he gave in San Diego in March 2012. Watch the video and then find out at the conference what further data adventures Bill has had with his kids in the last year and a half.
The Quantified Self Global Conference will be held in San Francisco on October 10th and 11th. Registration is now open. As with all of our conferences, our speakers are members of the community. We hope to see you there!
At our Quantified Self conferences we focus our show&tell talks on personal, first person narratives of self-tracking and self-knowledge. But what if first person is actually two people instead of one? Well, that’s when things get interesting!
At the upcoming Quantified Self Global Conference we are excited to have Dr. Rosane Oliveira talking about her self-tracking experiments that she’s been conducting with her twin sister. This great talk will include explorations of genetic testing, metabolic biomarkers, gut microbiome and mobile monitoring of diet, weight, sleep, mood, and activity levels.
Lucky for us, we’ve already received a preview of sorts in the form of a wonderful talk Rosane presented at the Bay Area QS meetup group in March of 2012. Watch the video below and come prepared to learn more about what you Rosane was able to learn when she started tracking with her genetic double.
The Quantified Self Global Conference will be held in San Francisco on October 10th and 11th. Registration is now open. As with all our conferences our speakers are members of the community. We hope to see you there!
Earlier this year we discussed some very interesting research from the Pew Research Center’s Internet & American Life Project about the role of technology and the Internet in health and healthcare. We were lucky to have Susannah Fox, Associate Director at Pew, talk to us a bit about what it means when 21% of people who track are using some form of technology. Of course, that conversation and that research spawned a few more questions and some interesting insights.
Today we’re looking at some brand new research results coming from Pew that are derived from that same research data set. This time Susannah and her team have focused on a particularly important set of individuals in the health and healthcare space: caregivers. In their recently released report, Family Caregivers are Wired for Health, they found that 39% of adults in the U.S. are caring for child or adult. So why talk about this here? What does that have to do with Quantified Self? Well, it turns out that the people who spend their time and energy caring for the health and wellbeing of others may actually be more engaged in tracking than their non-caregiving counterparts:
- 72% of caregivers track their health (weight, diet, exercise, blood pressure, sleep, etc.) while 63% of non-caregivers track their health.
- 44% of caregivers who track say they track their most important indicator “in their heads” (non-caregivers = 53%).
- 43% of caregivers who track say they track their most important indicator using paper (non-caregivers = 28%).
- 31% of caregivers track the health of someone other than themselves.
“When controlling for age, income, education, ethnicity, and good overall health, being a caregiver increases the probability that someone will track a health indicator.”
- 41% of caregivers who track share their data with someone else (non-caregivers = 29%).
- 52% of caregivers who track say it has changed their overall approach to maintaining their health or the health of someone for whom they provide care (non-caregivers = 41%).
- 50% of caregivers who track say it has led them to ask a doctor new questions or to seek a second opinion (non-caregivers = 32%).
- 44% of caregivers who track say it has affected a decision about how to treat an illness or condition (non-caregivers = 26%).
We asked our friend and fellow QS organizer, Rajiv Mehta to comment on this report. When he’s not helping organize our Bay Area QS Meetup, Rajiv has been working on exploring and understanding caregiving.
“Given the prevalence of caregiving (40% of adults) and that 30% of caregivers track something about the person they’re caring for, there’s a lot of opportunity for appropriate tracking and analysis tools. However, caregiving often involves tracking a wide variety of medications, biometrics, symptoms, etc., and design and developing appropriate tools is not easy. I recently wrote about my own experiences in “Self-Care and Caregiving Apps Development.” After all these years of QS meetups and conferences, I can only recall one talk of caregiver tracking (a mother tracking the progress of her baby). Hopefully we’ll see much more over time.”
Please take some time to read the full report and for the data savy, take a look at the preliminary survey data and see what you can find. We would love to hear your thoughts on this new report here in our comments or on our forum.
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