Tag Archives: data

Doug Kanter on Data, Diabetes, and Marathon Training

Doug Kanter has been a Type 1 diabetic for 26 years. Through this time he’s come to learn more about his disease by using many data-gathering tools and his own work in visual analysis at the NYU ITP program. We’ve featured Doug’s compelling work here on the blog before and we were excited to hear him talk at the NY QS Meetup about his new project to understand how marathon training and running effect his blood sugar and insulin treatment.

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What We Are Reading

Here we are again. Another week and another great list of articles, projects, and posts. We hope you find these as interesting as we did.

Data Science of the Facebook World by Stephen Wolfram: “I’ve always been interested in people and the trajectories of their lives. But I’ve never been able to combine that with my interest in science. Until now.” Stephen Wolfram sets his mind and data crunching services and the mounds of data available through the Wolfram|Alpha Personal Analytics service.

There’s an App for That by John-Paul Flintoff:  While many people write about QS, every once in a while a piece stands out as a thoughtful and personal assessment of the meaning of self-tracking. The only major fault with the piece is the accompanying illustration which proclaims that “the overexamined life is not worth living,” a conclusion the article does not actually make.

Disciplinary Power, the Oligopticon and Rhizomatic Surveillance in Elite Sports Academies: Elite athletes and sports programs push Quantified Self tools to their extremes. This article from an academic journal about surveillance discusses the tracking mechanisms employed in elite sports academies that transform performance into a type of numerical language that contributes to new social norms, personas and senses of the self

Refugees of the Modern World by Joseph Stromberg: A common cultural signature in the world of the Quantified Self is the formation of loose-knit groups around common interests and conditions. So it was fascinating to learn of a tight-knit group that has formed around the choice of a common environment in which to live. This is the stort of a self-diagnosed group suffering from “electromagnetic hypersensitivity” who live together in an area of West Virginia in the U.S. National Radio Quiet Zone.

Body 01000010011011110110010001111001 by Stanza: Artists have been playing with connecting #quantifiedself and “smart city” technologies for several years. I think projects like this are useful for opening new channels of thought not yet constrained by utility.

Goggles Can Provide Vital Data and Distraction by Matt Ritchel: Google makers incorporate data streams into heads up displays. But why include text messages? That seems like a mistake.

Thanks  to Joshua Kauffman and Gary Wolf for contributing to this weeks post! If you’ve found something interesting be sure to send it to us and we can post it in the upcoming weeks.

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Self Expression From Performance Data

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Typically when we think about Quantified Self and the associated collection and visualization of personal data we’re left struggling in the world of charts, graphs, and other well-worn visualizations. That’s not to disparage those of you who love spending some time tinkering in Excel. Those are valuable tools for understanding and there is a good reason we rely on them to tell us the stories of our data. It’s important to realize that those stories rooted in data aren’t always just about finding trends, searching for correlations, or teasing out significant changes. Sometimes data can represent something more visceral and organic – the expression of a unique experience.

Vincent Boyce is a an artist and designer who spends his free time riding on asphalt and water. Those experiences on his longboard and surfboard led him to starting thinking about how his rides, his performances, could be used as inputs for generating art and “exposing the hidden narrative.” After some tinkering with hardware and software Rideware Labs was born. Vincent has designed and built a prototype sensor pack and custom interface that ingests data from his riding and outputs unique visual representations. As you can see above, these aren’t your typical bar charts.

In his great talk filmed at the New York QS Meetup Vincent describes his motivation behind building his prototype system and his goals for future versions.

This is a great first step in turning data rooted in performance into artistic representations of self-expression. What do you think? What kind of data would you like to see hanging on your wall as works of art? Let us know in the comments!

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How To Make A Sparktweet

Update: Want to make your own Sparktweet? We made a simple tool that you can use. Check it out here!

I was stumbling around Twitter the other day when I was confronted with something new and different:

Apparently that little data representation is not all that new and different. Way back in 2010 Alex Kerin figured out that Twitter was accepting unicode and decide to play around and see if it could represent data. Lo and behold it could and a SparkTweet was born:

Before we get into how you too can start populating your Twitter feed and Facebook (I checked and it worked there as well) with representations of your own Quantified Self data let’s dive into some history.

The data visualization theorist and pioneer, Edward Tufte, is primarily responsible for the widespread use of sparklines. In his wonderful his book, Beautiful Evidence, Tufte describes sparklines as

a small intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphic can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic.

In another wonderful book, The Visual Display of Quantitative InformationTufte describes sparklines as “datawords: data-intense, design-simple, word-sized graphics.“ Of course, those of us in the QS community are deeply interested not only in data, but also in how data operates in society, what is means as a cultural artifact that is discussed and exchanged in language both written and verbal. This interest iswhat initially  piqued my curiosity.  The movement of data and a dataword distributed among text and publicly expressed in a tweet. I can’t help but wonder, what does this mean for how we think about and express data about our world?*

How To

Update:Thanks to our QS friend, Stan James, you can now make Sparktweets right here on Quantifiedself.com. Just head over to our Sparktweet Tool page and start making your own “data words.”

If you want display quantitative data in your Twitter stream it shouldn’t take you all that long to get started. Lucky for us Alex Kerin has provided a nifty little Excel workbook that will generate the unicode that can be pasted into your tweet. Just download this workbook and follow the simple instructions! Soon you’ll be able to send out tweets just like this:

For those of you with a bit more technical skill Zack Holman has made a very neat command line tool that will quickly generate the unicode for sparklines.

Now you’re ready and able to go forth and tweet your data! If you use a sparktweet to express your Quantified Self data be sure to let us know in the comments or tweet at us with #sparktweet and/or #quantifiedself.

*Of course the use of sparktweets is not without controversy in the world of data visualization. For more discussion on sparktweets and their utility I suggest you start here.

 

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How To Download Fitbit Data Using Google Spreadsheets: An Update

If you’re like me, then you’re always looking for new ways to learn about yourself through the data you collect. As a long time Fitbit user I’m always drawn back to my data in order to understand my own physical activity patterns. Last year we showed you how to access your Fitbit data in a Google spreadsheet. This was by far the easiest method for people who want to use the Fitbit API, but don’t have the programming skills to write their own code. As luck would have it one of our very own QS Meetup Organizers, Mark Leavitt from QS Portland, decided to make some modifications to that script to make it even easier to get your data. In this video below I walk you through the steps necessary to setup your very own Fitbit data Google spreadsheet.

Step-by-step instructions after the jump. Continue reading

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Vahe Kassardjian and Rafi Haladjian on Crossing the Data Desert

Today’s breakout session preview for the upcoming QS conference is from Vahe Kassardjian of INM and Rafi Haladjian of Sen.se. Below they describe their session “Crossing the Data Desert:”


Some people base decisions on facts and data, while some people base them on other foundations or beliefs.  Let’s immediately forget about this latter group.

For the former group, the quantity, frequency and overall reliability of the collected data is very important.  However, capturing data seems harder in real life than it sounds.  In addition to resolving technical challenges, such as designing reliable devices that provide usable data, the persistent action of collecting data for further analysis is sometimes a burden that people just don’t feel like carrying all the time.

In this breakout session, we will focus on people who get excited about the novelty of using a Fitbit device or a Runkeeper app to track their workout (the “Discovery Stage”). These are the people who diligently log the food they ingest into an iPhone app for a few weeks before they get a feeling that they are wasting their time. At some point,  they feel that the benefit they get from logging data is much lower than the cost of the effort required to do it.

We hypothesize that the novelty effect behind these self-quantification efforts fades away in a few weeks or months and falls in a “Data Desert”.  Whoever survives the crossing of the Data Desert is eventually rewarded with enough data and knowledge to pragmatically leverage them for effective decision making (“Data Legacy”).  But abandonment is more likely than persistence during this difficult phase.  Oftentimes, these people don’t have a vital problem to solve by collecting data.  They are curious to uncover a vague concern, but no drastic and immediate consequence will ensue from stopping data collection.

Our hypothesis relies on personal observations of quantified-selfers (including ourselves) and also on the phenomena observed in subscription-based services (such as joining a gym) as well as on Geoffrey Moore’s “chasm” principle.

The proposed goal for this breakout session is to explore strategies, designs, technologies and incentives to help people persist in their data collection after the initial enthusiasm fades away.

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Talking Data With Your Doc: The Doctors

One day you decide to lead a data-driven life and naturally data collection seeps into the realm of health. Maybe you buy a Zeo to better understand your sleep patterns. Or maybe you decide to start tracking your blood pressure with one of the various new connected tools. Heck, maybe you’re just tracking your daily pain symptoms using plain old paper and pencil. Whatever it is you’re tracking, most likely you have the urge, the need, to take it to your physician or medical provider. That data represents you, the whole you, not just the you that sits on that sterile paper that’s rolled onto the examination bed in the cramped room with the poor lighting and six-month old issue of Time magazine.

Last week we discussed how you could present that information, your health data, to your doctor. The wonderful Katie McCurdy helped us understand the power of simple visualizations for her ongoing care and her own personal health knowledge. The patient perspective is incredibly important, as they say, “everyone is a patient at some point.” But, not everyone sits on the other end of the table, not everyone is a doctor. So what do physicians think about patient data? What do they see happening in their practices? Today, we’re lucky to have two wonderful physicians join us to offer their insights into those questions and more.

Dr. Eric Topol is an innovator and pioneer in the fields of wireless medicine and genomics. 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. He is also the author of the recently released book, Creative Destruction of Medicine.

Dr. Larry Chu is a practicing anesthesiologist and an Assistant Professor of Anesthesia at Stanford Medical School. He also directs two separate research labs at Stanford – the Opioid Physiology Lab and the Anesthesia Informatics Lab. In his spare time (the man doesn’t sleep!) he directs the efforts for the upcoming MedicineX conference: a showcase of academic research, new technology, and patient stories designed to help guide the future of healthcare. I highly recommend watching their newly released e-patient videos that highlight two QS community members – Sean Ahrens and Hugo Campos.

Both Dr. Topol and Dr. Chu were kind enough to lend some of their time to answer a few of our questions. I hope you enjoy their thoughtful answers as much as I did.

QS: We keep hearing horror stories about doctors reacting negatively to patients who bring in their own health data. Why do you think that is?

Dr. Eric Topol: It reflects “old medicine” which is the current standard of care, characterized by paternalism, the “medical priesthood” and “Doctor Knows Best.” This will change and desperately needs to change to a participatory partnership of the patient and physician.

Dr. Larry Chu: I’m not sure that I can speak for other doctors, but I can tell you that I not only encourage my patients to self-track, but that I actually use those information streams every day to make decisions about each patient’s care. I treat patients with chronic pain. I have my assistants call my patients each day to get their pain scores, and ratings of medication side effects. I also ask my patients to keep daily diaries of their pain scores, side effects, and ratings of their ability to do activities of daily living. Working together, we use this information to tailor daily adjustments to their medications that I think not only improves their overall care but makes us better partners in a process that aims reduce pain while minimizing medication side-effects. Self-tracking brings me new data about each of my patients on a daily basis, which gives me new information and ideas on how to continually improve their care.

QS: How can a patient more appropriately create a dialog about their self-tracking and health data with their health provider(s)?

ET: By simply collecting the data and finding a receptive physician. Most doctors are data-driven and many would be enthusiastically supportive. 

LC: I think it starts with sharing data that helps doctors understand what they want to know. Your doctor might ask you, “How’s your back pain been since I saw you last?” That might be the perfect opportunity to share pain diaries (or even a visualization of pain scores over time) to help your physician understand trends in symptoms and how they are affected by medications and other factors such as activity and exercise. 

Data overload is a concern, so a focus on presenting concise data relevant to your physician’s interest might be a good way to start introducing self-tracking data into your physician visits.

QS: You illustrate a lot of examples of how the digital revolution is foundation of the Creative Destruction of Medicine in your book. What are a few fundamental shifts you see happening in the near future (1y? 5yrs?)

ET: 1 yr-the introduction of a large number of biosensors and “adds” that are smartphone centered and measure most physiologic metrics, or perform medical diagnostic tests like skin scan, refracting the eyes for glasses, and so many more; rapid sequencing for rare conditions, cancer at the time of initial diagnosis for genomically guided thearpy. 

5 yr—marked change in the basic structure of the office visit with more Skype, Facetime, video chatting and less need of hospital beds except intensive care units—with the marked reliance on remote monitoring; routine genotyping before many drugs are given to avoid serious side effects and assure the drug will indeed be effective

QS: 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?

ET: Yes, without question, anything that makes it more reductionist, simple, and less time consumptive.

LC: Absolutely. Physiologic processes have natural variability between patients and when tracked prospectively over time. Very rarely in my practice do I treat a “number”. I find that trends in data over time are the most useful in helping me understand physiologic processes in order to provide a diagnosis and therapeutic care plan for my patients.

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.

ET: There will be a Darwinian selection process for the “digital doctors” who have the plasticity to engage with patients in this way.

LC: Paternalism in medicine will hopefully diminish as physicians see that patients not only prefer but demand to participate and engage in their own care and that this engagement leads to better partnerships that produce better health outcomes. Self-tracking data and visualizations can help support that process. One example is medication compliance in my area of pain management. The very term “compliance” is a bit paternalistic because it implies that patients are expected to “comply” with a physician’s “orders”. If Mrs. Jones has been “non-compliant” with her medications, the reason might be more complicated than a simple failure to follow directions. Self-tracking allows me to see what happened: nausea and itching were out-of-control and limited her ability to increase her dose, or she had several high-activity days that exacerbated her pain. Self-tracking data, especially real-time streams that are passively collected with high resolution and granularity, have the potential to disrupt the paternalistic view of the patient-physician relationship. To me, that’s very exciting.

QS: Katie McCurdy mentions in her 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?

ET: Her remarkably careful and detailed self-assessment of her myasthenia gravis condition is prototypic of how data can be displayed. Our big mission is to reduce the work involved in capturing and graphing the data, but instead to have this done seamlessly. No question that data are good for one’s health. It’s the kind of data we did not have access to before in treating patients. Sensors, apps, and add-ons to smartphones will help to streamline this process.

LC: Make providers part of the process. Give us an opportunity to let you know what data we would love to see you track. Help us understand your concerns and how we can help you achieve your health goals. 

QS: What tips or advice would you give to someone who is taking their data to their doc for the first time? 

ET: Go for it! Don’t be shy. It’s your data, your body, your health. You are the most vested and important individual for the future of your health!

LC: Start with a picture, something simple, that helps your doctor better understand your body in relation to the reason for your visit. Data overload is a concern. Start by turning the spigot on slowly.

QS: How do you think self-tracking and data communication with physicians can support patient-initiated health experimentation?

ET: It will be the N of 1 story to find the right drug for conditions like high blood pressure or diabetes (Type 2, non-immune) and many other conditions. Moreover it will be invaluable for prevention, for which we will have a marked enabling capacity once we integrate genomics, sensors, health IT, the digital infrastructure and N of 1 —what I call Homo digitus–data! 

LC: I think self-tracking can provide real-time physiologic and symptom data to physicians to aid them in interpreting the success of patient-initiated health experiments. I use self-tracking to study physician-initiated health experiments in my NIH-funded clinical research lab at Stanford. I don’t see a reason why the tables can’t be turned.

We also have some questions from Susannah Fox, who was kind enough contribute her thoughts and insights to this piece:

SF: True or false: There have always been patients like Katie, who try to figure out what’s going on with their health. It’s just now that they have tools to polish up and express their observations in engaging ways. It’s just now that clinicians are ready to listen to and even welcome such patients.

ET: True for many conditions.

LC: True. We have been self-tracking even before there was the term. I got to mark my height on my door frame every birthday growing up: I was a self-tracker at age five! There is a temptation to focus on technologies and tools in self-tracking, but they are not a necessity for the process. A pen and paper will suffice. What I see today is an explosion of consumer-facing devices, some of which passively collect high-resolution and finely granular datasets. This may add to the data streams we can collect, but analysis and synthesis of the data into meaningful conclusions is a growing challenge.

SF: If you are observing a shift, in yourself or in your colleagues, why do you think that is?

ET: I have shifted my practice, but unfortunately I have not seen a significant shift in many others yet. That’s why I wrote the book—to educate, activate consumers to catalyze “new medicine”  They need to drive this—it is their medical information, their DNA, their tissue, their smartphone, and their social networks. Never before were we so well positioned for a consumer health care revolution as now. A veritable Kairos.

LC: I think mobile computing and wireless mobile devices have exposed physicians to many of the same consumer-facing self-tracking applications that their patients use. As patients ourselves, many physicians see the potential for self-tracking to impact our own health and lives.

Having read through these answers again and again I can safely say there are some major themes that are starting to creep up. Partnerships, excitement, mHealth – each of these concepts were mentioned on more than one occasion by these two amazing members of the medical establishment. Hopefully their insights will help give you the small push to begin speaking to your medical provider about your health data. The data you collect. The data that represents you.

Again, this is part two 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 Susannah Fox next Thursday. If you have questions of comments feel free to discuss on FacebookTwitter, and here in our comments.

 

 

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Talking Data With Your Doc : The Patient

Data.

Health.

Communication.

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.

Katie's Medical Timeline

Katie was kind enough to answer a few questions and we’re grateful to be able to share her responses here with you today.

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.

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Personal Informatics In Practice: Digital Histories for Future Health

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.

 

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Michael Schoeffler on Noisy Data

Mike Schoeffler from Roadbud talks about the effect of noisy data on self-quantifying. A popular GPS running app had been giving him trouble – magically teleporting him and missing parts of his runs. He found it frustrating enough that he built his own app. Watch the video below to see the interesting discussion with the audience that cropped up around whether or not noisy data is really a problem. (Filmed at the NY Quantified Self Show&Tell #12 at ApK Media.)

Mike Schoeffler – Roadbud from Steven Dean on Vimeo.

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