As part of my CHI 2014 paper reading series, today I read “Personal Tracking as Lived Informatics” by Rooksby et al. from the University of Glasgow in the UK. They offer a compelling take on activity tracking. It is a good read, even if a bit contradictory at times, in my opinion.
Idea: Understand people’s practices and relationships with activity tracking devices and personal tracking in everyday life. This paper claims that personal tracking has been studied very narrowly so far, where devices serve as nothing more than activity detectors or as part of interventions.
Approach: Unstructured and follow up interviews with 22 participants.
Findings: Tracking is social (but not in the ’share on Twitter/Facebook’ social, there are different styles of tracking (directive, documentary, diagnostic, reward-based, gadget-based), multiple trackers are used, tracking information used for meeting daily or short-term goals.
Notes: People chose different metrics to examine out of the devices they used, data collected and analyzed for short-term use (people not concerned about saving the data), tracking punctuated memorable life experiences (e.g., finishing a marathon), self-esteem. Idea that tracking is actually almost always prospective, since it’s about where one’s heading (e.g., losing weight, training for a marathon.
Together with Jon Froehlich, Jakob Eg Larsen and Matthew Kay, I am organizing a Ubicomp workshop later this year. The theme: disasters in personal informatics. Here is a synopsis of what we are trying to accomplish:
In this workshop, our goal is to uncover, analyze, discuss, and learn from the failures of PI and QS research—failures that are most often not captured or surfaced in traditional publications because of embarrassment, perceived irrelevance, or simply lack of space. We want to provide an explicit forum to share stories of failure, perhaps even entire lines of research that did not succeed, in order to synthesize lessons learned and help progress the PI research community forward.
More details can be found on the official workshop web site. Please considering submitting – I believe this will be a rewarding experience for all who are working in the Personal Informatics space.
Last week I had the opportunity to attend the Quantified-Self Public Health Symposium organized at UCSD by Bryan Sivak (U.S. Department of Health and Human Services), Larry Smarr (Calit2 and ), and Gary Wolf (Quantified Self Labs). The intent of the one-day event was to provide a conducive environment for researchers, policy makers and developers of QS-type tools to discuss issues around data and its potential for use in public health. Many attendees such as Susannah Fox have written about the highlights of the day from their perspective. Here are my notes:
At the beginning of the day, Larry Smarr said he believes this is the “year of scale” when it comes to personal health informatics. Stephen Downs (Robert Wood Foundation) reiterated this point with a remark about our “rapidly expanding oceans of personal health data” and expanded on how RWJ Foundation has identified that a cultural change is needed around the value of health in society. Gary Wolf pointed out the uniqueness of the researchers involved in QS, ranging from public health epidemiologists and CS/HCI types, and also claimed that we are at “the beginning of a new program in the human sciences”, which I very much agree. Susannah Fox from the Pew Research Center made a number of excellent points based on the data she has been collecting and studying. When it comes to self-quantification, do “people want the unforgiving bright light of numbers in front of them”?, she asked. Fox also talked about how it is important to respect cultural rituals.
Later in the day, in a session reserved for “toolmakers”, Anne Wright from Fluxtream discussed how personal health data should be as portable as personal financial data, where one can download documents from banks and upload them to tools such as Mint and Quicken. In terms of visualizing and making sense of data, she mentioned the need for different types of interfaces at different levels (e.g., personal dashboards versus deep history introspection). Margareth McKenna from Runkeeper brought with her a number of research questions that are difficult to answer even in light of large amounts of data, such as what happens when people are not logging their exercise and activities (“sensors can only capture so much”). She also emphasized the need to disseminate tools to everyone, echoing others during the day who talked about the “digital health divide”. Andy Hickl from A.R.O. presented the “11-week problem”, at which point self-tracking devices go to the drawer. In other words, “what should we care?”, or how can we make self-tracking data meaningful? At the end of the session, Gary Wolf came back and proposed a research toolkit that could be shared within the community.
In one of the sessions focused on study designs, a highlight for me was Eric Hekler, a behavior psychologist and deep thinker around behavior change. He discussed how he lost faith in randomized clinical trials and how we should be asking people what they truly want from data and devices. He also made the point that we should support people to come up with their own interventions and find out ways to “get more functional life years”.
To me, the high-point of the day was a panel with Larry Smarr and Lee Hood. Hood, whose work has revolutionized biology and genetics, is leading a longitudinal, Framingham-like study with the goal of better understanding how digital technologies and ongoing self-tracking can be leveraged to quantify what it means to be healthy. Another goal of the study is to look at the progression of disease, as observed by these devices. Smarr, one of the most recognized self-trackers in the Quantified-Self movement today, started his tracking journey to understand his own personal health issues, in a way doctors could not, or were simply unwilling to listen.
In addition to the plenary talks and discussions, the day was filled with coffee breaks, where I had the chance to chat and exchange ideas with many researchers and developers, such as Kevin Patrick from UCSD and Mike Lee, who leads MyFitnessPal. Overall, a great day in the campus of UCSD.
Have you heard of the AIRO? It is the latest device in the sea of activity and “well-being” trackers. This one deserves a little bit more attention because it claims to be able to automatically track not only sleep and exercise like the Fitbit and Up, but also stress and food intake. For the latter, the device is supposed to have an embedded spectrometer that can break down the nutritional intake of food consumed – this is what makes it stand apart. All in all, the AIRO is what the millions of people interested in tracking have been waiting for, the one wearable that tracks the key pillars of health: diet, stress, sleep and exercise. But is it real?
I am extremely skeptical. It all sounds good in theory, but in practice it’s a completely different story. First, there is food. Friends who are in the biomedical engineering space tell me that detecting “metabolites” through spectrometry is a promising direction, but unlikely to be developed enough to be productized by 2014. And based on my own research, I question the value of obtaining this information automatically. There is increasing evidence that when it comes to food, it is critically important that people are actively engaged in the food journaling process. Awareness of what one eats, and not just background data collection, is what leads to behavior change.
Second, there is stress. There are lots of researchers working on ways to capture stress level in naturalistic settings. Galvanic skin response, heart rate variability (HRV) and voice features have been used to estimate emotional state. This is not my area of my expertise so I cannot comment in much detail, but again, it is a really hard problem, particularly when it comes to evaluating the technology. Stress is highly subjective and variable from person to person and not all forms of stress should be perceived negatively.
Finally, we have sleep. Some believe that an accurate hypnogram can only be obtained through polysomnography. Thus, the notion that a smartphone app or wristband can wake you up at the perfect time so you feel as refreshed and rested as possible might be fundamentally flawed. A more detailed examination of this topic can be found here.
I hope the AIRO team proves me wrong on all these points.
While doing some research in preparation for my thesis proposal, I decided to re-read one of the papers that I expect to become increasingly important in the field of personal informatics. It is Li et al.’s “A Stage-Based Model of Personal Informatics Systems“.
Based on surveys and interviews with 68 people, the authors suggest a model that describes personal informatics systems as a series of 5 stages (Preparation, Collection, Integration, Reflection, and Action). One of the most useful aspects of the paper in my view is the description of barriers people encountered in each stage. Some of the barriers in the Collection stage include “Remembering”, “Lack of Time”, and “Motivation”. These map exactly to the challenges that I’ve identified in my recent food journaling work.
If you are interested in the space of personal informatics, it is a paper I recommend. You can download it here.
At TEDMED this year, Deborah Estrin made a strong case for leveraging our “digital breadcrumbs” to “help us get a clearer picture of our personal health”. She referred to these “breadcrumbs” as our “small data”, and told the story of her father’s decaying health; while invisible to his caregivers and doctors, his changing daily behaviors could have been picked up by his phone and computer. She finished her talk with a call-to-action: we need to get web services and mobile providers to give us our data back so that we can use it for our own purposes, such as for personal health inferences.
Needless to say, I agree with Estrin. Collecting digital traces of our lives is something that I have been doing for almost 10 years now, first with Slife Labs and now in my research. In fact, being plugged-in to this concept has forced me to pay attention, and as a result, I’ve seen many efforts with similar messaging come and sometimes go (hello Attention Trust, YouData, the Locker Project). In my opinion, however, we don’t necessarily need access to what is centrally stored about us by AT&T, Verizon and Google to make an impact. By working at the node level, by installing software on our phones, computers and soon TVs, we can collect all the data we need to build predictive models of human behavior. In fact, this has been done, and Estrin knows this too – she mentions the company Ginger.io in her talk. Additionally, many of these companies already provide APIs that give us access to a lot of the data we would want. From my experience, the biggest barrier to realizing the small data vision that Estrin proposes is getting the green light from the medical community, to not only to validate and refine these approaches but also incorporate them into traditional medical practices. That’s where the challenge lies; I do not see access to data as a barrier, at least not at the personal level.
Another significant health initiative related to personal activity data tracking was announced yesterday. The California Institute for Telecommunications and Information Technology (Calit2) in San Diego and the Robert Wood Johnson foundation unveiled a project called Health Data Exploration, with the goal of bringing everyday activity data to researchers. In the spirit of the Quantified-Self movement, it is framed as a conversation, an exploration, and not as a research program per se. But if I am correct, the ideal outcome would be to have companies like FitBit, Jawbone and Nike open their activity databases for “the public good”.
There is a clear overlap between these two initiatives. I see the value of the enormous amount of data that companies like Fitbit have compiled. Analyzing that data would certainly lead to new insights about health and human behavior, so I am curious to see how the project unfolds. But again, as a researcher, today I feel that I am more restricted due to the skepticism of the medical community towards adopting new strategies and technologies than by lack of data. Even starting collaborations with medical professionals has been a challenge.
Here is what would be very exciting to me as a computer scientist working in the health domain: an avenue to work directly with doctors and patients. I am not sure how this could be structured, but in an age when health accountability matters, it might be possible to incentivize healthcare institutions to work alongside technologists more closely. Yes, I am aware of the difficulties associated with the regulatory process, getting FDA approval, dealing with HIPAA, etc, but my perspective is that we will have to deal with these hurdles eventually no matter which road we take. In practical terms, I would love to see a resource, perhaps a web site, acting as a meeting place where electrical engineers, biomedical engineers, computer scientists and even designers could propose studies to healthcare organizations around new and innovative technologies and approaches. One of these studies could be Estrin’s small data, perhaps applied in the context of congestive heart failure or Alzheimer’s Disease. Doctors and caregivers would visit this resource and possibly sign up to collaborate, depending on their needs and areas of specialization. Realistic? I am not sure, especially considering that physical presence matters in these kinds of collaborations. But to me the value of a project like this is crystal clear. Especially if doctors and scientists really do show up to the party and decide to dance together.
It’s been about a week since I got back from Paris after attending CHI. This was the most productive CHI for me personally, which I attribute in large part to the Personal Informatics workshop I attended.
The workshop, which took place on Saturday and Sunday before the conference began, was an opportunity to spend time and exchange ideas with a great group of folks. Thanks to Ian Li, Jon Froehlich, Jakob Eg Larsen, Catherine Grevet and Ernesto Ramirez for putting it together. It required lots of work to organize it, since it was set-up as a two-day hackathon. All the effort paid off nicely in my opinion. The interesting question now is how to make the Personal Informatics community within CHI grow, since it has outgrown the workshop format.
Now on to the main conference. With 10+ tracks, it is always a challenge to navigate CHI. Here’s a timelapse of my day 1 at the conference:
I was able to attend some good/interesting presentations, such as:
Validating a Mobile Phone Application for the Everyday, Unobtrusive, Objective Measurement of Sleep, Lawson et al.
Designing Mobile Health Technology for Bipolar Disorder: A Field Trial of the MONARCA System, Bardram et al.
Footprint Tracker: Supporting Diary Studies with Lifelogging, Gouveia and Karapanos.
Mind the Theoretical Gap: Interpreting, Using, and Developing Behavioral Theory in HCI Research, Hekler et al.
NailDisplay: Bringing an Always Available Visual Display to Fingertips, Chao-Huai Su, Liwei Chan, Chien-Ting Weng, Rong-Hao Liang, Kai-Yin Cheng, Bing-Yu Chen
Food Practices as Situated Action: Exploring and designing for everyday food practices with households. Rob Comber, Jettie Hoonhout, Aart T van Halteren, Paula Moynihan, Patrick Olivier
Not surprisingly I was particularly drawn to the health-focused talks, but there were some intriguing and inspiring topics of interest in many other sessions.
Thanks for the inspiration Paris. Next year, Toronto.
A couple of weeks ago I finally made some time to read Larry Smarr’s perspective paper titled “Quantifying your body: A how-to guide from a systems biology perspective“. Smarr is a leading figure in the Quantified-Self movement and someone who has been engaged in multiple levels of body monitoring for several years. The paper describes his own extensive self-quantification experience that eventually led to the discovery that he has Crohn’s Disease (CD). If you don’t have time to check the paper, you can find a short summary of the story (and a video) here. But I do recommend the paper, it is a quick, easy read.
At its core, Smarr’s story is about personalized preventive medicine, the type of health care that many of us envision and hope that becomes commonplace in the not-too-distant feature. However, what becomes apparent in the paper, as Smarr describes the various levels of body monitoring, starting with nutrition and delving into system biology and genetics, is how much complexity must be exposed and understood before one can bring all the pieces together and make sense of what is truly happening with the human body. Which brings me to the title of this post. Beyond the diagnosis and collection of health data, which generates much of the excitement nowadays, to realize the vision of personalized preventive medicine for the average person, one of the crucial steps will be to design interfaces that help people navigate the avalanche of health information that can and will become available to them.
I am particularly fond of the concept of health as a “fuel”, a “resource for living”, which was put forth by the World Health Organization many years ago. To my mind, it most closely matches the way most people think of health – as a means to an end. Therefore, if I am correct, it is highly unlikely that most people will invest heavily in the details and intricacies of enzymes, hormones and biomarkers, no matter their predictive power and how important they prove to be for people’s health and well-being. The opportunity that lies ahead, and that must be addressed, is to design and build an interface platform for personal health data at all levels of monitoring that leads to insights, is perceived as valuable, and that can be used by anyone. A tall order for sure. Back in the mid-nineties, we thought that the web was the common platform that would unite us all. And we were excited about the possibilities then as we are now about the future of health care. But today we know that it took a highly simplified, standardized, and shrink-wrapped version of the web (i.e. Facebook) to bring us to billion-level scale in terms of adoption.
Two days ago we got a report from the Pew Research Center’s Internet & American Life Project on health tracking. Ernesto Ramirez had a chat with Susannah Fox, an Associate Director at the Center, and published some data and their conversation at the Quantified-Self web site.
One of the interesting findings to me was the percentage of people in the study who track health data informally, “in their heads”. I wrote a comment in the QS site about this, and I partially reproduce it here:
From the point of view of health literacy, we might be making more progress than we think. It could be that these people are just really inefficient at being healthier, but the motivation is there. This is analogous to the 10:00 min/mile runner who wants to get faster. There’s tons of low hanging fruit – a tempo or interval run here and there will probably bring the time down significantly. Compare that to the 6:00 min/mile runner who has to work a lot harder to shave off those extra seconds. So, overall I think this is good news.
Yesterday I had a chance to read a story that discusses an important aspect of activity tracking; these devices might encourage us to exercise and walk more, but we don’t know yet what their long-term impact might be. The article claims that various institutions (hospitals, universities, healthcare organizations) are conducting trials to determine how digital data trackers affect activity levels, obesity and dietary intake in various populations. I am really looking forward to reading more about these studies.
As it’s quite obvious from my previous posts, I’ve been thinking quite a bit about the large number of activity tracking devices that have been released in the last few months. Literally, it seems like a week doesn’t go by without the release of a new wristband, or watch, or mobile app that claims to track our activities and help us get healthier. But the truth is, we have a lot to learn. This is one of those cases where products are being developed because we have the technology to build them, and people seem interested in buying them.
There are two ways of looking at this. One one hand, we don’t have a solid scientific foundation in place to support this trend quite yet, at least not in the traditional sense. At the same time, as many self-quantifiers would say, the rules of the game are changing, and n=1 is what counts.