Tag Archives: PEW
We hope you enjoy this week’s list of articles, posts, show&tell descriptions, and visualizations!
I’m Terrified of My New TV: Why I’m Scared to Turn This Thing On — And You’d Be, Too by Michael Price. Michael, a lawyer at the Brennan Center for Justice at the NYU School of Law, describes his experiences with his new “smart” TV. More sensors means more records being stored somewhere you might not have access to. Especially interesting when your device picks up every word you say:
“But the service comes with a rather ominous warning: ‘Please be aware that if your spoken words include personal or other sensitive information, that information will be among the data captured and transmitted to a third party.’ Got that? Don’t say personal or sensitive stuff in front of the TV.”
Public Perceptions of Privacy and Security in the Post-Snowden Era by Mary Madden. A great report from the Pew Research Internet Project. I don’t want to give away any of the juicy stats so head over and read the executive summary.
This Is What Happens When Scientists Go Surfing by Nate Hoppes. It’s not all privacy talk this week. This is a fun article exploring how new sensors and systems are being used to monitor surfers as they train and practice.
How Private Data is Helping Cities Build Better Bike Routes by Shaun Courtney. We covered the new wave of personal data systems and tools feeding data back into public institutions a bit before. Interesting to hear that more cities are investing in understanding their citizens through the data they’re already collecting.
What Do Metrics Want? How Quantification Prescribes Social Interaction on Facebook by Benjamin Grosser. Ben is most commonly known around the QS community as the man behind the Facebook Demetricator, a tool to strip numbers from the Facebook user interface. In this article, published in Computational Culture, he lays out an interesting argument for how Facebook has created a system in which the users, “reimagine both self and friendship in quantitative terms, and situates them within a graphopticon, a self-induced audit of metricated social performance where the many watch the metrics of the many.”
The Cubicle Gym by Gregory Ferenstein. Gregory was overweight, overworked, and in pain. He started a series of experiments to improve his help, productivity, and wellbeing. I enjoyed his mention of using the Quantified Mind website to track cognition. If you find his experience interesting make sure to read a previous piece where he explains what happened when he replaced coffee with exercise.
Maximizing Sleep with Plotly and Sleep Cycle by Instructables user make_it_or_leave_it. A really nice step by step process and example here of graphing an making sense of Sleep Cycle data.
Toilet Matters by Chris Speed. A super interesting post on what a family was able to learn by having access to data on of all things, the amount of toilet paper left on a roll and when it was being used. Don’t forget to read all the way to end so you can get to gems like this:
“[…]the important note is that the source of this data is not only personal to me, it is also owned by me. We built the toilet roll holder and I own the data. There are very few products or smart phone apps that I can say the same about. Usually I find myself agreeing to all manner of data agreements in order to get the ‘free’ software that is on offer. The toilet roll holder is then my first experience of producing data that I own and that I have the potential to begin to trade with.“
E-Traces by Lesia Trubat. A beautiful and fun project by recently graduated design student, Lesia Trubat. Using adruinos and sensors places on the shoes of dances she was able to create unique visualizations of dance movement. Be sure to watch the video here.
Animated Abstractions of Human Data by James E. Pricer. James is an artist working on exposing self-collected data in new and interesting ways. Click through to see a dozen videos based on different types of data. The image above is a capture from a video based on genotypes derived from a 23anMe dataset.
The Great Wave of Kanagawa by Manuel Lima. Although this is an essay I’m placing it here in the visualization section because of it’s importance for those working on the design and delivery of data visualizations. Manuel uses the Great Wave off Kanagawa as a wonderful metaphor for designing how we visually experience data.
D3 Deconstructor by UC Berkeley VisLab. A really neat tool here for extracting and repurposing the data powering at D3.js based visualization.
Personal data, personal meaning. That’s the guiding principle of much of the work we do here at QS Labs. From our show&tell talks and how-to’s, to our worldwide network of meetups and carefully curated unconferences, we strive to help people make sense of their personal data and inspire others to do the same. However, over the last few years we’ve started to see that there is a third actor in the Quantified Self space. Data collected in the ordinary course of life can hold clues about some of our most pressing questions related to human health and wellbeing. Personal data might be a resource for public good.
On April 3, 2014 Quantified Self Labs with support from the Robert Wood Johnson Foundation, the US Department of Health and Human Services, and Calit2 at UCSD hosted the first Quantified Self Public Health Symposium. We gathered over 100 researchers, toolmakers, science leaders, and pioneering users to open up a discussion about what it means to use personal data for the public good. Over the course of the day we hosted a variety of talks, discussions, and toolmaker demonstrations. This week we’ll be highlighting some of the outstanding talks delivered at the symposium and we’re kicking it off with one of our favorites.
Susannah Fox has been a friend and colleague for many years. Her pioneering work at the Pew Internet and Life Project has inspired us many times over and remains the standard for research pertaining to self-tracking. We asked Susannah to help us open up the meeting by discussing some of her research findings as well as her thoughts on self-tracking in the broader landscape of health and healthcare.
(A transcript of Susannah’s talk can be found on her website here.)
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.
(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.
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.