Tag Archives: Health
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.
Stuart Calimport is on a quest to find the most useful memes for health and well-being. He started the Human Memome Project, and spent a year and a half collecting all his ideas about health. He classified 5137 of these ideas as healthy/ethical/optimal and 6581 of them as unhealth/unethical/sub-optimal. In the video below, Stuart shares some examples of his memes, as well as his process for optimizing meme rate generation, and what he has learned about himself on this adventure. (Filmed by the London QS Show&Tell meetup group.)
Some people may be wondering how I find all the amazing people conducting neat self-tracking experiments and creating jaw-dropping personal data visualizations. Well, for the most part I just listen. I’m constantly paying attention to what’s being said on twitter about #QuantifiedSelf. When that doesn’t work I just use the power of Google to find people who are blogging about self-tracking, self-experimentation, or personal data. It’s great to look through the search results and see how many people are sharing their personal stories and insights. While doing some searching this morning I stumbled across a project that immediately brought a smile to my face. Hopefully you’re excited by this as much as I am.
Chris Volinsky is a statistician at AT&T Research and he’s no stranger to handling large data problems. Back in 2008 he was part of the team that won the $1 Million Netflix prize. He also has quite the impressive list of research papers that illustrate the many different uses of cellphone location data. But what is really interesting about Chris is his newest project: My Year of Data
Back in November of 2011 Chris started off a blog entry that with this:
My name is Chris. I am 40 years old. I am 5’9 1/2″ and weigh 174 pounds. I walked 9,048 steps and have consumed 1,406 calories today (so far).
Realizing that he’ld been gaining weight and wasn’t at his optimal health he decided to take a data-centric approach to improving his health. He is a statistician after all. So far, he’s found some interesting things. Take for instance his weight and dietary tracking.
As he explains in this post, Chris typically has a hard time tracking his diet consistently. This can be pretty frustrating when you hear about how important it is to eat this or not eat that to help with weight reduction. Rather than get frustrated Chris turned to the data to see what he could learn. When he stopped looking at the data he was entering and started looking at the missing data an interesting trend lept out. He found that fluctuations in his weight appeared to be correlated with whether or not he was logging food. Take for instance the plot below. It appears that there is a pretty clear association with periods of weight loss and periods of actively logging his food (pink zones). The opposite also appears to be true – no food logging = weight gain.
So this is where a typical NFATW post would stop. We have an interesting finding and a neat data visualization. But, Chris is doing something much more interesting than just talking about his weight data. He is on a long-term self-tracking and self-discovery journey and he is trying to enlist other interested parties to help him. Chris is going the extra step and posting all of his self-tracking data online for anyone to analyze, visualize, or just get inspired.
You can access all of his amazing data via a public dropbox folder that he’s set up. He even has a nice README file explaining the datasets and formats. So far he’s sharing the following:
- Fitbit: sleep and activity data
- FitLinxx: weight training data from gym activities
- Livestrong: dietary tracking data
- Runkeeper: running and other exercise activity data
- RescueTime: productivity tracking (computer/internet use)
All the data is open and available for you to play with. This should be a really interesting project to keep “track” of in the future (pun definitely intended). To help inspire some action on your part I took some time today and looked at Chris’s most recent available data to see what I could find out. I downloaded his Fitbit data and decided to look for any interesting patterns. Turns out that when taking a look at his daily patterns of activity there seems to be something going on on Thursdays that reduces his step count and activity time . Also, Saturday is by far the best day with an average of 9,862.56 steps and a 5.3 hours spent being active (data available here).
Make sure to reach out to Chris over at his blog and take a took at his data to see what interesting thing you can figure out!
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.
Sky Christopherson is a velodrome cyclist who has been on the U.S. Olympic team. After retiring, he lived in the world of startups, and when his health started to decline as a result of that stress, he turned back to the kind of quantification he had been doing as an athlete to restore his health. In the video below, Sky talks about what he learned, like how temperature affects his deep sleep and how his blood glucose fluctuates. He also shares the exciting news of setting a world record, at age 35, after his self-tracking experiment. (Filmed by the Bay Area QS Show&Tell meetup group.)
Health Mashups: Helping People Find Long-Term Trends Between Wellbeing and Activities in Their Lives
Frank Bentley is a Principal Staff Research Scientist at the Motorola Mobility Applied Research Center outside of Chicago, IL. He creates new mobile applications and services that help people connect with each other and with data about their lives. He then studies how these systems are integrated into daily life over weeks and months.
Do you sleep better on days when it’s warmer? Walk less on days packed with meetings? Gain weight on the weekends? A growing number of consumers are turning towards specialized devices that track particular aspects of their lives and wellbeing. Whether it’s the Zeo to track sleep, the FitBit to track daily step counts, the MOTOACTV to track workouts, or the WiThings scale to track their weight, there is currently a wealth of personal data that is being stored about daily activities. However, most of these services continue to be silos. Even where the ability to import data from one device into another’s service exists, data is only combined superficially, providing at most a graph of steps and weight over time, obscuring long-term and periodic interactions. The questions presented above cannot be answered without great effort – effort that many in the Quantified Self community devote to understanding themselves. But can it be easier?
We see the key value of tracking multiple aspects of one’s life to be understanding the interaction of data from wellbeing sensors with other sensors as well as with contextual data about a person’s life (where they spent time, how busy their day was, the weather, etc.). We want to enable people to discover these hidden trends in their lives without resorting to complex Excel files and a PhD in statistics.
The Health Mashups system
The Health Mashups system was built through a collaboration between KTH University and the Motorola Mobility Applied Research Center. It consists of a server that aggregates data from a variety of sensors and a mobile application to automatically capture a user’s context and display the resulting correlations calculated by the server. Users can connect their FitBit accounts for step count and sleep data as well as their WiThings account for weight data. An Android application uploads contextual information automatically each day including the number of hours busy on the user’s calendar as well as the current location at a city level and weather for that location. After the initial setup, no further actions are required from the user to keep this data flowing to our server (although we also support manual food and exercise logging through the mobile phone application). Each night, our server computes correlations between sensors and deviations on data from a given sensor and generates a feed of items that are statistically significant. This feed is then accessible on the phone or web for users to view and reflect upon. Users can see feed items such as: “You lose weight on weeks when it is warmer” or “Yesterday you walked much less than you normally do on Saturdays.” This eliminates the need for manual log books and messy Excel files, and opens Quantified Self-style investigations to those with no technical background.
We wanted to understand how a broad range of users would integrate this system into their lives. We conducted a two-month field trial and recruited ten diverse participants in Chicago and Stockholm to take part. They came from a wide range of ages and educational backgrounds and had a variety of reasons for participating: from particular issues with sleep or excessive weight that they wanted to address to a general curiosity to understand themselves better. Participants were given a FitBit and a WiThings scale and asked to use these in their lives for the first month. Whenever they had an insight about their wellbeing, they were asked to call us and leave a voicemail describing their insight. For the second month of the trial, they were given the Health Mashups interface on their phone and again were asked to call us with new insights.
For the first month of the trial, none of our participants called with insights across sensors or time scales. While many reported general trends (e.g. “I’ve been losing weight this week” or “Yesterday I didn’t walk as many steps as I thought I did”), their insights did not connect their sleep, weight loss, or step counts to each other in any way. Nor did they include insights about patterns on specific days of the week or comparisons/deviations from week to week.
In the second month, participants were able to understand their wellbeing in much deeper and complex ways. The system showed them insights across sensors and varying timescales. Our participants reported understanding and relating to these feed elements. The mashups data helped our participants to better understand how aspects of their lives were related and to make positive changes in their lives (e.g. eating a little less fried chicken on Sundays or walking more on specific days of the week).
The Future of Health Mashups
We see a promising future for personal data analytics related to one’s wellbeing. With massive amounts of wellbeing and contextual data now being collected, systems are needed that make sense of this data for people and allow them to focus on what is significant to their lives without a large amount of effort. With Health Mashups our participants could gain these insights, combining data that is automatically collected as they live their lives. We believe these types of insights have the power to raise awareness about situations that lead to poor life choices, resulting in positive changes in behavior and ultimately happier, healthier lives. This summer we will be conducting a larger quantitative study to investigate the impacts of this system across a wider group of participants. If you are interested in participating, you can register your interest here.
In our daily lives, we are keenly aware of the power of each of these individual concepts. However taken together, their influence on our wellbeing, to borrow a phrase from my friend Karen Herzog, “our wholeness”, is exponentially influential. So why do they seem to rarely coalesce during our conversations, discussions, and interactions with the individuals and institutions tasked with tracking, diagnosing, and treating the cracks and fissures in our wholeness?
This is the first in a three-part series about the data we produce about our health and how we communicate that information to the medical system, specifically the providers of care. We’re starting from the perspective of the patient because we’ve all been there. Whether it was a routine check up or a 3AM visit to the emergency room, we’ve all had to relay information to a medical provider about out health. So what happens when we’ve collected, stored, and tried to understand our own health information in preparation for those visits?
Our guide today for the patient perspective of health data communication is Katie McCurdy. Katie is a user experience designer and researcher living and working in New York. She is also living with Myasthenia gravis, an autoimmune disease that causes muscle weakness in voluntary muscles. Like many individuals with autoimmune diseases, Katie spend a lot of time communicating and working with the medical system. These visits, although regular, were a point of contention between Katieand the individuals entrusted with her care. So when she was going to see a new physician for the first time she decided to apply her interaction design knowledge and skill. She’s talked about this on her blog and on the e-patients.net blog so I’ll let here words speak for themselves:
As I was getting ready to see a new doctor, I realized that the best way to tell my story would be to create a medical “life story” timeline that reflected:
- The course of my autoimmune disease
- Severity of my gastrointestinal problems
- Key moments in time when I started and stopped certain medications or took antibiotics
- Any significant dietary changes
I sketched out the two timelines (autoimmune and gastrointestinal) separately, and then created them electronically using Adobe Illustrator. (I’m an interaction designer by day, so fortunately I had the skills/know-how to create a somewhat legible artifact.) I used a peach color to represent gastrointestinal wellness/symptoms, and a blue color for Myasthenia Gravis.
QS: Why visualize? Do you think doctors are more receptive to the visual translation of data rather than the raw numbers that are commonly associated with health data?
KM: For me it’s about creating a representation of my history and my health that can be communicated most efficiently. I believe in the power of visualization to help tell stories that wouldn’t be possible with raw data alone. Knowing I would be ‘on the spot’ during my doctor visit put the pressure on to make something that would help me tell my story as succinctly as possible. Also…because I was not tracking my data (it’s all from memory) I didn’t have the raw data to share anyway!
QS: I’ve been thinking about the doc-patient relationship a lot lately. It seems the walls of authority are crumbling as we speak and we’re moving from a “You do this” or “You listen to me” type of authoritative approach to medicine to more conversational. How do you see data and visualizations helping to start and possibly support those conversations.
KM: I see it as, like you said, changing the dynamics of the relationship so that the patient is more of a partner in care. By tracking data, the patient can provide a more refined and nuanced picture of what is really going on with them. By visualizing that data, the patient is helping the doctor absorb the information more painlessly. The patient is providing contextual information about his or her OWN situation that compliments the doctor’s past experience, expertise, and test results.
QS: You mention in your post that the reception from patients and caregivers has been really positive, how would do we help make it a positive and rewarding experience for the providers as well?
KM: I think that giving patients tools to create simple, clean, and attractive visualizations could help make the experience better for doctors. If doctors are presented with high-quality visualizations that tell a coherent story, it may make office visits more efficient. Imagine if the doctor could work with the patient and suggest a type of graph or visualization that would be most helpful.
QS: What tips or advice would you give to someone who is taking their data to their doc for the first time?
KM: I suggest using the data as a storytelling tool. Bring a printed artifact or something on a tablet to refer to, and point out the highlights as you talk about what’s been going on with you. Don’t be disappointed if they don’t comment on your beautiful data and all of the work you put into it. Ask if there is anything you can do to to make the data more legible/easy to understand for the doc.
QS: You mention that self-tracking has given you better insights into your own health and that you’re even trying some self-experimentation like a no-carb diet. How do you think self-tracking and data communication with physicians can support patient-initiated health experimentation?
KM: Ah, I think self-tracking and visualization can help increase patient compliance! My low-carb diet was actually prescribed by my doctor. When I saw on the timeline that my diet changes were strongly correlated with my gastro symptoms improving, it was very reinforcing of my diet behavior. I mentioned antibiotics in my post. Now, if I even think of asking for antibiotics, all I can see in my mind is the number of antibiotics I took as my stomach issues got worse and worse. That is a big change in my outlook that resulted from internalizing the data I was seeing on the timeline.
QS: Who are your design/data viz heros? Anyone who really inspires you in your health visualizations?
KM: I have a few data viz heros! Jer Thorpe, of the new york times, makes beautiful interactive data visualizations and is one of the best speakers I have ever seen. Nicholas Felton, of Feltron and now a designer at Facebook, is a compulsive self-tracker who releases a gorgeous printed yearly report. I love Mortiz Stefaner’s work as well. I am really inspired by the natural world and the work of 19th century plant and wildlife documentor Ernst Haekel. I am also inspired by the awesome patients I’ve met and the folks on e-patients.net who remind me that patients need to be their own advocates.
We also have some questions from Susannah Fox, who was kind enough contribute her thoughts and insights to this piece:
SF: Would Katie care to comment on that from her own experience? That is, is it only recently that she has both found the right tools and that her own clinicians are interested? Had she attempted something earlier, with pencil & paper? What has made the difference?
KM: I never did anything before this apart from bringing notes to my doctor visits – things to remember to say. I literally had a realization one day at work and wrote an email to my personal account with the subject: ‘very important idea.’ :) I think the idea had to incubate for a few years before it bubbled up.last fall.
My goal is to keep pursuing this idea and work toward creating a tool for patients so they can at least assemble their own health timeline, and perhaps even track their data more regularly. I am holding interviews with patients, patient caregivers (or parents), and people who are active self-trackers; if you are interested in donating about 30 minutes of your time, email me at kathryn.mccurdy at gmail.com.
Again, this is part one in a three-part series on the data centric conversation we engage in with the medical community. Look for our next part with insights from Dr. Eric Topol and Dr. Larry Chu next Thursday. If you have questions of comments feel free to discuss on Facebook, Twitter, and here in our comments.
Ben Ahrens got his start in self-tracking as a personal trainer for six years. He was then diagnosed with Lyme disease and spent two years in bed. In the video below, he talks about his tracking failures, the importance of intuition and simplicity, and what he learned about controlling his symptoms by tweaking his mental state. A courageous, honest, insightful story. (Filmed by the New York QS Show&Tell meetup group.)
Matt Velderman wanted to figure out his acne problem. He dove into researching acne treatments, tracking himself and modifying his diet and behavior. His approach was to try every possible thing that could help at once to solve the problem quickly, and then remove one thing at a time to figure out a minimal set of interventions. In the video below, Matt describes everything he tried, odd side effects, and how it’s working, as well as some other QS projects he’s doing, from bodybuilding to home energy usage. There’s a fun discussion at the end about how tracking yourself can impact romantic relationships. (Filmed by the Washington DC QS Show&Tell meetup group.)
Every day you interact with the web. You log on. You upload, you download. You tap and you click. You search, you “like”, you pin, and you retweeet. These actions make the web work for you, but they also make you work for the web. It should come as no surprise to even the casual technology observer that we are now living in the age of data. Some call it “big data”, but instead of thinking about it as a thing, we can also think of it as a an ecosystem that can be described by its fundamental structure – the database. Our lives and the actions we engage in on a daily basis are constantly being accessed and stored in a database. Our actions may be passively collected (think about how Google’s Adsense operates) or actively collected (checking in on Foursquare or updating Twitter). While it may seem as if we are living and engaging with a dystopian ecosystem, we believe that there are possibilities for engaging and enhancing our current health experiences by taking advantage of our personal and social databases.
We don’t need to rehash the idea that we are also in the midst of an explosion of tools and services that support the gathering of health-related data. If you’re reading this, you know that the Quantified Self movement is gaining traction and new devices and applications are being introduced at a rapid rate. Naturally, these tools are heard towards helping an individual lead a healthier life. This inherently creates a future-focus environment in which the user is presented data, analytics and recommendations for positive health behavior change in the future. This is typically accomplished through two methods, information on current behavior and goal progress information. We argue that many of these tools and services are not taking full advantage of the vast amount of information that is available to them.
The wide-spread proliferation of application programming interfaces (APIs) that allow developers and users to access large amount of data opens up numerous possibilities for possibly improving the health and behavior conversation between a user and his or her tools/system of choice. We foresee unique opportunities to use historical behavioral data, contextual information (e.g. location, social interactions), and health actions to highlight patterns and provide feedback through three mechanisms: 1) reminders of success, 2) behavioral prompting, and 3) contextual reminders.
The road to good health is not an easy one and there are numerous examples of individuals who unfortunately lapse into negative or poor behavior patterns. We are proposing that when “failure” points are identified there is an fantastic opportunity to remind the user of previous success. Reminding a user that they have had success in the past may help to limit self-doubt and reductions of self-efficacy. The psychological burden associated with failing to meet goals could be quickly replaced with a positive a reminder of the user’s mental and physical capability that is based on objective historical information. Instead of just having an empty “You can do it!” we envision future services that say, “We believe you can do it because, look, you’ve done it before!”
We also see the potential for building upon the concept of modeling illustrated in social learning theory and social cognitive theory. While modeling is typically thought of in the social sense, we propose that services can use historical data and contextual information to create powerful and meaningful representations of a user (maybe as a digital avatar). By presenting a user with their past self they can use it as a tool for comparison (“What am I typically like?”) or competition (“How can I be better than my previous self”). Imagine, for example, waking up in the morning and seeing your past self and associated behavioral data in your bathroom mirror or on a display on your refrigerator. We believe that this past self could act a positive guide to help you lead a healthier life.
Lastly, the large amount of information stored in your behavioral databases has an inherent ability to converge and provide information about contextual factors associated with behavior. For example, we can easily find out if you get more or less steps on days it is raining or if you tend to eat worse when you check in to airports around dinner time. Using simple data mining and contextual linking it is possible to identify positive behaviors patterns and bring them to light. By tapping into the rich digital histories being captured and stored across many services we may not only help a user remember, but also enhance their ability to celebrate and re-enjoy healthy behaviors.
Too often, we encounter warnings of services tracking out behavior and using if for their own personal gain. It is time that we ask the tools and applications we use to help us lead healthier lives by taking full advantage of the vast amount of historical information we are collecting. The Spanish philosopher, George Santayana told us, “Those who do not remember the past are doomed to repeat it.” Our increasing digital lives allow use to not only remember the past, but harness that powerful information to help us lead better, healthier lives.
This article is a summary of a position paper by Ernesto Ramirez and Eric Hekler that will be discussed at the Personal Informatics in Practice workshop at CHI 2012 in Austin, TX on May 6, 2012. The workshop will be a gathering of researchers, designers, and practitioners exploring how to better support personal informatics in people’s everyday lives.
We here at Quantified Self Labs wanted everyone to know that tonight (Feb 7th, 2012) Gary Wolf will be speaking in San Diego on a panel with Dr. Eric Topol, Larry Smarr and Dr. Joseph Smith about “Quantified Self and the Future of Personal Health.”
The panelist for the event include:
Gary Wolf is the co-founder of The Quantified Self, a global collaboration among users and makers of self-tracking tools. His is also a contributing editor at Wired magazine, where he writes regularly about the culture of science and technology. His work has appeared The Best American Science Writing (2009) and in The Best AmericanScience and Nature Writing (2009). In 2010, he was awarded the AAAS Kavli Science Journalism prize. In 2005-2006 he was a John S. Knight Fellow at Stanford University.
Larry Smarr is the founding Director of the California Institute for Telecommunications and Information Technology (Calit2), a UC San Diego/UC Irvine parnertship, and hold the Harry E. Gruber professorship in Computer Science and Engineering at UCSD’s Jacobs School. Dr. Smarr has recently been profiled by Xconomy about his ‘10-Year Quest for Quantified Health‘
Dr. Joseph Smith is the Chief Medical Officer and Chief Science Officer of the West Wireless Health Institute, Dr. Joseph M. Smith leads initiatives to identify and accelerate the use of health care innovations and technologies to advance the Institute’s mission of lowering health care costs.
Dr. Smith has an extensive career at the intersection of clinical medicine and engineering. Prior to joining the Institute, he was most recently Vice President of Emerging Technologies for Johnson & Johnson in their Corporate Office of Science and Technology. He also served as Senior Vice President and Chief Medical Officer of Guidant / Boston Scientific, Cardiac Rhythm Management.
Dr. Eric Topol is an innovator and pioneer in the fields of wireless medicine and genomics. In addition to his serving as Vice Chairman of the West Wireless Health Institute, he is the Director of the Scripps Translational Science Institute, a National Institute of Health funded program of the Clinical and Translational Science Award Consortium. He is also Professor of Genomics at The Scripps Research Institute; Chief Academic Officer and holder of the Gary and Mary West Chair of Innovative Medicine at Scripps Health; and, a Senior Consultant cardiologist practitioner at Scripps Clinic. Dr. Topol has been elected to the Institute of Medicine of the National Academy of Sciences, named as one of the 12 “Rock Stars of Science” in GQ, Top 100 Most Influential People in Healthcare in 2011, and is recognized by the Thomson-Reuters Institute of Scientific Information to be in the Top 10 cited biomedical researchers in medicine in the past decade. He is also the author of the recently released book, Creative Destruction of Medicine.
Although this event has been sold out for those in the San Diego area, CALIT2 is able to stream a live webcast of the event. If you would like to tune in please set your calendar reminders for 7PM PST and follow the instructions below. Once you’ve downloaded the appropriate software be sure to tune into the live stream at: http://calit2.net/webcast.
Calit2 On-Demand Streaming in Windows Media
Calit2 webcasts require Microsoft’s Windows Media Player or compatible software and a broadband Internet connection. Our typical webcast stream runs at 512kbps at 640×480 pixels.
Mac users can download a free software program called Flip4Macthat will allow their Quicktime player to play back Windows Media formats.
Troubleshooting playback on a Mac?http://www.flip4mac.com/support_wmv.htm
We will also be taking questions via twitter during the event. Please use the #FHSD hashtag if you would like to ask the panelists a question during the Q & A period.