Topic Archives: QS 101

QS 101: It is Not About the Tools

There has been an exponential rise in the number of people talking and writing about Quantified Self. Some call it a movement, some call it “the next big thing.” In most, if not all cases, there is a an overwhelming emphasis on the role of technology. Be it new sensor systems, applications, or analytical tools, there is an interesting need to equate Quantified Self with technology. It should come as no surprise then that when people start asking me about Quantified Self one of the first questions I hear is, “What device should I buy?” or “What is the killer app/tool/service for QS?” Maybe this is something you’re asking yourself so let’s talk a little bit about how tools fit into the Quantified Self experience.

Think about the last home improvement project you started. Whether it was fixing a leaky faucet or replacing your carpet you most likely went about your work in a simple step-wise fashion: 1) Identify the problem, 2) Examine possible solutions, 3) Identify the most appropriate solution, 4) Gather the right tools to implement the solution, and then 5) Fix the problem. Tools don’t come in to equation until late in the game. I think the same can be said for your self-tracking or self-experiment. The tool is not the piece that defines what you should be tracking or what experiment you should run. It is merely there to help you gather information that is necessary to produce a new piece of knowledge. And that is the point of this whole endeavor – creating new knowledge. Unfortunately, this is often overlooked because in most cases knowledge isn’t as sexy as a new shiny wireless device.

So if tools are not the end-game here, what is? Let’s take a quick look at the Three Prime Questions:

  1. What did you do?
  2. How did you do it?
  3. What did you learn?

Those three simple questions are great guiding principle for Quantified Self and your own personal self-experimentation. You’ll notice that technology isn’t mentioned in our methodology (what some consider to be a simplified scientific method). In fact, the most important aspect of this methodology, and where we recommend you start your self-experimentation journey, is the last question: What did you learn? Perhaps it is better to phrase it this way, “What do you want to learn?” What is the question that has been nagging you lately. What lifehack, productivity secret, or health tip have you come across and wondered. “Is that true?” or “Will that work for me?” This is where all good experiments start. Whether it is a million dollar experiment in a renowned university lab or a personal experiment that starts in your kitchen, the production of new knowledge starts with a good question. 

Only after you’ve identified and refined your question should you begin to look into tools that will help you produce the information that helps you develop the understanding that may lead to an answer. You may even want to develop a methodology or experimental plan before identifying what tools works best for you. In any case, keep in mind that the goal of self-experimentation, of Quantified Self, is to produce and share new knowledge.

 

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QS 101: The Science of Self Experimentation

This special guest post in our ongoing QS 101 series comes to us from Dan Gartenberg, our great QS-Washington DC meetup organizer, and his fellow graduate students in the Human Factors and Applied Cognition program at George Mason University.

Turning Scientific Concerns into Strengths for Quantified Self Experimentation

By Dan Gartenberg, Ewart de Visser, and Jonathan Strohl

Quantified Self and Science are not oil and water. They are intertwined with one another and have a long history together. Though some scientists may not hold QS in high regard, and have the following claims:

“these studies lack validity!”

“a study of a single individual will not generalize to the broader population”

“the possibility of experimenter bias makes your findings highly suspicious and inconclusive.”

“is your effect even real?”

While these are legitimate concerns, QS is science.  And if we keep in mind the scientific method when conducting QS research, this strengthens the validity of our QS projects.

History speaks for itself.  QS studies are actually in line with an age-old scientific tradition: The n=1 study. Back in the day, scientists did not have large labs and took to experimenting on themselves. For example, Hermann Ebbinghaus, one of the first cognitive psychologists, conducted experiments on himself to reveal the process of learning and forgetting.  As a scientist he used a level of rigor that was expected of scientists at the time, and more importantly, Eddinghaus contemplated reasonable mechanisms that explained his results.  Science gives us the tools to make precise measurements, and QS, with its emphasis on improvement of the self, provides a social framework for people to discuss novel phenomena.  In this article we demonstrate how science lends itself to QS and how the scientific method provides us with useful tools for self-discovery.  We first recommend a framework for conducting QS experiments and then discuss the scientific methods to keep in mind.

1)  Achieve your goal:  Unlike most experimental research, in QS our main objective is more often than not self improvement.  A frequently used approach to improve yourself is by throwing the kitchen sink at the problem until you get the sought after effect.  When we make this process social, we can then discuss with others what they are doing and how they think they are being affected by what they are doing.  Based on this information, we get a better understanding of how to most effectively modify our behaviors for the desired outcome.

2)  Use a simple design.  When presenting your data the scientific establishment might criticize your conclusions because it was not the gold standard “double blind randomized control trial.”  But running the right type of design isn’t the be-all-end-all of good science.  If you see a difference and have a reasonable mechanism that explains the difference, with no viable alternative explanations – you are solid.

3) Stats don’t matter as much.  Just graph it! One of the biggest sources of confusion is how to analyze QS data and knowing the right stats to run.  But statistics are only really useful when predicting small effects or for more complex prediction models.  In QS, any change is usually meaningful.  For example, if you are tracking your mood and you see a small improvement it is likely meaningful to you.  So don’t worry too much about statistics.

To discuss the roles of QS and science, we’ll use a dataset that we generated as a case study.  After reading the 4 Hour Body by Tim Ferris, three friends all had the same goal of losing weight.  None of us were extremely overweight at the time, but we could all stand to lose about 10-pounds.  This inspired us to create the 10-Pound Challenge, where we competed to lose weight and either did a slow carb diet or a low carb diet.  We then weighed ourselves every morning.

Quantified Friends: The 10-Pound Challenge:

Here are some issues and concepts that you should consider when making sense of your findings and how Science and QS can benefit one another:

Threat to validity Definition 10-pound application
Mortality  When your manipulation affects the likelihood of whether or not you respond to the measure of interest (i.e. you don’t respond to a survey out of embarrassment). There are almost no skipped days over the course of our study.  This demonstrates how QS can be used as a way to address the problem of mortality by making data collection more social.  This makes people more accountable and motivated to input their data.
History When external events from the environment impact the variables of interest. The diet was made social when we shared our progress with one another. This socialization, where we competed to lose weight, may explain our progress.  We knew that this resulted in alternative explanations, but it didn’t matter because we simply wanted to lose weight and were pulling out all the stops to help us reach our goal.
Maturation When over the course of a study you have changed in other ways that have confounded the impact of the variables in your experiment. In QS our goal is to actually change and mature.  In our QS project the intention was to lose weight and the mechanism was only as important as it was necessary to understand and use in order to promote our increased weight loss.
Treatment Fidelity  In science we usually compare a treatment group to a control group, but what if the treatment is not much different from the control? (i.e. there is not a good counterfactual). We administered relative treatments based on each of our unique situations (this is a common issue with QS).  For example, one of us already had a relatively strict diet.  He was able to make precise modifications to his diet in order to promote weight loss, whereas; the other two QSers made broader changes. This prevented us from making precise claims about how the diet affected weight loss.  Though we still got the general idea that the diet worked.
Treatment Interaction  When the variable that you manipulate interacts with other variables that explain the outcome. When undertaking the 10-pound challenge we frequently told people about the challenge.  This in turn made us more accountable for what we consumed due to social pressure.  In this example, the response to the treatment interacted with the social environment in a way that made us consume healthier meals and lose weight.  Since in QS we are not as fixated on control, we can see how these interactions unfold in the environment and discuss them with others in order to confirm or deny our intuitions.  This provides us with ways to explore new ideas and mechanisms.
Compensatory Rivalry  When the control group is aware that they are not getting the treatment and in turn seeks out other alternatives. In the case of the 10-pound challenge, there was no control group.  Control groups do not play a large role in QS because of the focus on self-improvement.  This is an issue that can be addressed by the scientific method of an A-B-A design where the QSer acts as their own control group.
Regression Towards the Mean  This intuitive premise from science is the basic idea that at the extremes of a behavior you are increasingly likely to gravitate towards the mean. QSers should be particularly sensitive to this because people frequently try to improve on a behavior when they are at an all time low or an all time high.
Reactivity  When your response is affected by external factors, for example, social desirability. In QS, reactivity can actually be used to improve upon outcomes. In our example, there was a social desirability to discuss what was and was not working.  At one point the team members independently agreed that eating too many slow-carbs (legumes) was hindering their progress.  We then made the appropriate changes to our behaviors and found increased improvements.

And these are just some of the threats to validity that QSers can consider to improve their projects. So look out for more to come!

We would like to thank Dr. Patrick McKnight and the MRES group (http://mres.gmu.edu) for providing helpful insights on our QS project.

 

 

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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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QS 101: Make It Visual

So here we are with post #4 in the QS 101 series. We’ve already talked about keeping it simple, using the SMART system, and using social support to help you in your self-tracking process. Today we’re going to talk about what to do once you’ve collected your data – make it visual.

The Visual Cortex

You see, we humans are primarily visual animals. A large portion of our brains are dedicated to processing and deciphering the world we see. It makes intuitive sense that when it comes to self-tracking that we spend time creating images, charts, graphs, and visualizations that represent our collected data. One of the great things about our brains, especially our visual cortex, is that it is very, very good at recognizing patterns. Pattern recognition is a key aspect of the self-tracking practice. Being able to identify and recognize patterns related to behavior, thoughts, location, etc. helps us to start to tease out the intricate patterns that make up the complex cause and effect game we call life.

 

But Aren’t Numbers Enough?

Glad you asked! While we all love the numbers we generate from our different self-tracking methods, being able to see a visual representation of the data allows us to look deeper into the intricacies of the numerical values. Consider for a moment the wonderful example provided by English statistician, Francis Anscombe. Let’s consider the following four graphs:

You can immediately see the difference among the four graphs, they obviously represent very different characteristics of a measured phenomena. What’s interesting here, and what Anscombe’s Quartet demonstrates, is that simple statistics can be woefully inadequate for understanding datasets. Funny thing about each of those graphs – the data displayed has the same summary characteristics across each graph. That is, each graph has the same mean, variance, correlation and regression line. Only when the data is plotted can differences among the data be observed.

What Now?

There are a variety of tools and services out there to help you visualize your data, generate patterns and bring insight to your data – and we’ve highlighted them many times before here on the QS Blog. Hopefully looking back through these previous posts will help you and inspire you to spend some time making your data visual!

Do you have a favorite visualization or data viz tool? Share it in the comments below!

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QS 101: Make it Social

You’ve all heard the buzzwords being thrown around these days, “social media”, “social networking” etc. With the explosion of Facebook, Twitter, Foursquare and other social services it seems like you can’t go anywhere online these days without being bombarded with buttons yelling at us to “Share this!”, “Like This” or “Send this to a friend”. Why the proliferation of social sites and services? All of this shouldn’t be a surprise, after all we humans are social creatures. Rarely do we exist in complete isolation. Cliches like “No man(woman) is an island” are so popular because, well, they’re just true. So how does this relate to your Quantified Self practice?

You are a product of your social environment. We’ve known in the behavior sciences for a long time that the actions of one person can impact the actions of another. One of the most common concepts we’ve used to explain this is social support. To put it simply the social support represents the idea that we our behaviors are influenced and supported by our social structure (friends, family, colleagues, etc.). There are four fundamental types of social support that have been identified as being beneficial for starting and/or maintaining a behavior (follow the previous link for more detailed descriptions):

Emotional support – empathy, understanding, and caring  from others. 

Tangible support – material assistance (money, goods, tools, etc). 

Informational support – guidance, both subjective and objective knowledge. 

Companionship support – inclusion in a social group. 

One of the great things about Quantified Self is that we attempt to provide these four types of support at our meetups around the world and at our annual conferences. I’ve personally been able to find all four over the course of the last year and a half and consider myself immensely lucky to have found caring and smart people willing to support my self tracking journey. But, maybe you don’t have a meetup in your area or you’re not comfortable asking for support from a fellow group member, then what now? Well, one of the ways to enlist social support is to just ask someone who you trust and feel comfortable with to help you. This can be a major step for most people, but in most cases it is a step worth taking.

Briefly, the take away here is that when you are starting or looking to continue in your self tracking practice it is worthwhile to consider eliciting social support from others. Although we call ourselves the Quantified Self the notion of “self” does not mean our practices must be done in solitude. In fact, we celebrate and encourage informational support through our Guide and the Quantified Self Forums. We work hard to create collaborative learning and knowledge exchange and we’re always working on fostering the other aspects of social support, but I encourage you to look outside your local QS community to others in your social circles that may be able to provide you the support you need.

For a more specific example of the power of social support I highly encourage you to take the time to read the Transformative Power of Sharing Mood post by the wonderful Alexandra Carmichael.

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Quantified Self 101: Make it SMART

So here we are again with another QS101 post. I thought today I would walk you through a concept* that you may find useful for getting started on the path to self tracking. As a behavioral scientist I get a lot of people asking me about goals – how to set them and how to achieve them. I always fall back on a course I taught as a graduate student aptly titled, “Psychological Skills for Optimal Performance.” During that course I taught undergraduates different concepts related to sports and exercise psychology, and one of those was the SMART system. I think this system, beside being a clever use of an acronym, could be useful to your self-tracking practice.** So what does SMART stand for?

S is for Specific. When you decide to track something it is best to choose something that is specific rather than general. For example, you might be interested in your cardiovascular health and you decide you want to start tracking exercise. Well, exercise is a very broad category and can include activities like gardening to training for ultra-marathons. In this example you would be better served to track a specific type or method of exercise. For instance, you could use apps like RunKeeper to track your running or cycling, or you could use a pedometer to track your steps. The great thing about making a goal specific is that it allows you to find the right tool for the job. While you would be hard pressed to find one tool that tracks exercise, you can easily find a method for tracking your strength training activities or your swim laps.

M is for Measurable. You would think this would be a no-brainer, but it happens to the best of us – we forget that what we want to track has to be, well, trackable! Quantified Self is all about using the power of data to help you learn about yourself. When you decide to start along the path of self tracking it is vital to make sure that what you have decided to track can be measured in some way. In future posts we’ll talk about objective and subjective data collection, but for the sake of brevity let’s assume that you decide to use a tool or method that assigns numerical values to your behavior. Great! But, that is only the first part of making it measurable. You also have to take a step back and take a look at the data output(s) and decide if they make sense to you. For example, Gary likes simple 3-point scales to rate his feelings – good, bad, and okay make sense to him. Make sure that your measurement make sense TO YOU, because in the end YOU are what matters in this adventure.

A is for Attainable. Making your self-tracking attainable is a concept that is related to our previous QS 101 post on Keeping it Simple. So let’s assume you have the specific behavior down and you’ve decided how to measure it in a away so that it makes sense to you. It is now time to take a look at what it would mean to you and your daily routine to implement the tools/methods and data collection necessary to engage in your self-tracking plan. Simply put, is this something you incorporate into your life given all of other personal and social commitments. I, for instance, would love to track all of my writing for 2012 (email, twitter, research papers, etc.), but at this point the effort to engage in that task would take enough time that it would take away from more productive and enjoyable endeavors.** Making sure that your self-tracking practice is actually attainable is a good way to ensure that it remains enjoyable as well as informative.

R is for Relevant. The main focus of a self-tracking practice is to generate self-knowledge (look at our header it’s right under our logo). Knowledge generation for the sake of knowledge generation, while interesting, pales in comparison to knowledge generation that benefits you. You want to make sure that when you decide to engage in self-tracking that the insights you are looking for are helping you become your better self. For instance, I could track the number of times I open and shut my refrigerator and freezer doors. While this might give me some insight into what kind and type of food I consume (fresh vs. frozen) that data is probably less relevant to learning how to be my better self than tracking the types of food I consume by using a food diary or food image capture.

T is for Time-bound. This is probably one of the most overlooked and misunderstood aspects of self-tracking. By making your practice time-bound you are not necessarily stating when you start and stop your tracking-practice for a particular behavior of interest. Rather, you can use the idea of time-bounding to set parameters for when it is appropriate to delve into the data and go through the process of analysis and reflection. Setting this time parameter is very specific to you as a individual and the behavior you’re tracking. You may, for example, only need a week’s worth of food diary data to start to make some conclusions about how your diet is affecting your mood. On the other hand you may need to track your anxiety levels for a month to really understand how they correlate with your boss’s travel patterns. The actual time you decide to start the process of analysis and reflection isn’t important because you can always continue tracking after your first, second, . . . nth pass. What is important, is that you decide a priori (before the fact) when you will do it and then stick to that plan.

So there you go. Now that you know all about SMART you can starting using it to “optimize” your self-tracking practice. To get you started with conceptualizing your current or new self-tracking practice within the SMART framework I’ve created a simple worksheet you can use. It is available here for download here or you can access the google doc here. As always, feel free to post questions in the comments!

*This is only one concept for helping you think about self-tracking. We’ll be highlighting other methods and processes in the near future!

**I prefer calling my self-tracking a practice because it is an ever evolving process of doing, learning and refining.

***If you know of a way that I can accomplish this tracking task, capturing everything I write, in a simple and non-time consuming manner please let me know. You can email me here.

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Quantified Self 101: Keep It Simple

Here at QS Labs we’re here to help everyone, from the experienced researcher to the person who hasn’t done an experiment since they built that model volcano in sixth grade. We also try to listen to our community and we’ve heard many requests from individuals just starting their journey of self-experimentation. Well, I’m happy to announce a brand new bi-monthly section called Quantified Self 101. We’re going to be covering things like how to decide what to track, experiment design, bias, how to interpret your data, and other fun stuff. We also want to here from you. If there is something your struggling with or want to learn more about please leave a comment below or get in touch with us via twitter (@quantifiedself)

For our first post, we’re going to highlight some lessons from our friend Seth Roberts and his great talk on self-experimentation at Show & Tell #5:

Lesson #1: Something is better than nothing. Engaging yourself in some experiment, no matter how flawed it may be, is better than never starting. The best way to learn is to do. So go out and do something!

Lesson #2: When you decide to start something try and do the simplest thing that you think might give you some insight. It’s great to have ambitious ideas, but keeping it simple ensures your experiment is manageable.

Lesson #3: Mistakes are worthwhile. Some of our best knowledge comes from learning from our failures so don’t be afraid of failing. By keeping it simple you also keep the mistakes small and manageable.

Lesson #4: Seek help from others. We have a great network of individuals around the world who are ready and willing to help you on your tracking journey. Find a meetup in your area and don’t be afraid to solicit help!

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