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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Gary Wolf on MetaQS and Meditation

QS founder Gary Wolf speaks at the Silicon Valley QS meetup group, giving a meta look at what Quantified Self is about, followed by a personal show&tell about his meditation data.

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What We Are Reading

Happy weekend, everyone! Here’s a smattering of inspiring things we’ve been reading at QS Labs this week:

  • Seth Roberts’ series of posts on Vitamin D3 and sleep. The lesson: what time you take your supplements could matter a lot.
  • Transforming behavior change from the Social Brain Project at the RSA (UK): Some really interesting insights into behavior change and the role of neuroscience and reflexivity.
  • The latest issue of Bruce Schneier’s always interesting Crypto-Gram newsletter, with fascinating accounts of data breaches and hacking attacks, personal data vulnerabilities, and – for a bonus – an intelligent call to get rid of the United States’s Department of Homeland Security.
  • Schedule your creative tasks for when you’re most tired – a thought-provoking look at a circadian effect on creativity.
  • An opinion piece on the Research Works Act, the piece of legislation that threatens to roll back public access to federally funded research.
  • Smart Geotextiles for ground and building monitoring (from our friend David Pescovitz at BoingBoing.)
  • Transistors developed to monitor molecular processes - listening to enzymes. QS is moving to the molecular level!
  • Psychotropic Medications Affecting Biological Rhythms. (PDF) Looking at mood disorders and medications in the context of circadian rhythms as well as shorter and longer cycles will play an increasing role in good medical practice. This has applications to other health issues as well, and will require increasing self-awareness of empowered patients.
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Welcome Ernesto Ramirez!

Anyone who has presented their work at a Quantified Self conference will already know the amazing Ernesto Ramirez. Ernesto is a doctoral student at UC San Diego’s Center for Wireless and Population Health Systems, a leader of the San Diego QS Show&Tell meetup group, a regular blogger here, and a positive force inspiring people to live healthy lives. We are excited to announce that Ernesto has agreed to join us at QS Labs to share his goodness.

As our new Community Organizer, Ernesto will be helping to support new and existing QS Show&Tell meetups around the world, exploring social media channels to engage and cross-pollinate the global QS community, writing a “QS 101″ series to guide people new to self-tracking, and generally helping to keep our collaborative, listening culture strong.

You can expect to see and hear more from Ernesto in the coming months. He is also @e_ramirez on Twitter and Ernesto Ramirez on Google+.

Welcome, Ernesto!

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Numbers from Around the Web: Round 2

When we talk about Quantified Self and the meaning behind tracking there is always an underlying current that the numbers rule all. That there is a fundamental truth that is available and discoverable. But, in some cases self-tracking can take on different forms. For this week’s NFATW post I wanted to two projects that forsake the emphasis on numerical tracking for something different – visual tracking.

Hugo Campos
Hugo is a shining example of the e-patient movement (which I learned recently stands for empowered patient not electronic). His experience as the proud owner of an implanted cardiac defibrillator has led him to become an advocate for patient-centered data ownership and improved access to data derived from therapeutic medical devices. He’s also a big fan of Quantified Self and on more than one occasion has inspired me to be more active by engaging in fun FitBit step challenges. For the month of December, Hugo decided he was going to try and eat a vegan diet for the entire month, and document everything he ate by photographing his food. What followed was an amazing visual record of his dietary patterns. Take a look! You can click the images for the full Flickr set.
Flickr set Flickr set 1

I figure if it’s not worth photographing and sharing, it’s not worth eating. – Hugo Campos

Jeff Harris
Jeff Harris a photographer and 13 years ago he decided to begin an epic quest to document his life by taking on self portait every day. What follows is an amazing story of why he began this journey and the insights into his life that he’s learned along the way. I don’t want to give away any spoilers, but I will say that is well worth the six minutes to watch the video below.

We got such great feedback on the orignal NFATW post that we decided to turn it into a regular feature. 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.

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Toolmaker Talk: Eric Gradman (Facelogger)

This is the eight post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences?

For me, some of the most interesting QS talks have been by those creatively repurposing existing sensor technologies for novel self-tracking applications — such as Mikolaj Habryn’s Noisebridge, at an early QS meetup, and Hind Hobeika’s ButterflEye goggles, at the QS Amsterdam conference. It’s fascinating to hear what the inventors are thinking long before their product is in the market. Here, Eric Gradman, master hardware hacker, tells how he is applying his skills to a focused life-logging application.

Q: How do you describe Facelogger? What is it?

Gradman: The Facelogger is a passive lifelogger that helps me remember every person I meet by creating flashcards of their face, name, where we met, and our conversation. Facelogger consists of an always-on videocamera necklace, a software suite to process the video, and a smartphone interface for reviewing the flashcards.

The camera is a commercially available Looxcie camera, which was modified with a prism so it hangs around the neck. This camera continuously captures activity, and has a button that allows you to save the preceding 30 seconds of footage (footage that’s not saved is automatically discarded). When I meet someone for the first time and they introduce themselves, I press the button. The camera preserves the previous 30 seconds of footage which hopefully includes a good video frame of the person, their name, and what they said about themselves.

When I next plug the camera into the computer, all the captured video clips are automatically uploaded to a server, and sent to Amazon Mechanical Turk. There, human beings identify the most representative faces from the video, determine their names, and even transcribe the conversation.

Facelogger gathers all the information and creates a Facecard, which can be reviewed later on a smartphone. A Facecard is like a flashcard, but it shows someone’s smiling face, their name, a map of where you met, a link to the video of the conversation, and often even a transcript of the introduction. I  can search the text of the Facecards, sort them chronologically, or by geographic proximity.

Q: What’s the back story? What led to it?

Gradman: Like any self-respecting geek, I’ve always tried to stay a technological step ahead of my peers and a technological leap ahead of my parents. But when I discovered that my parents use the same model smartphone I do, I realized I was beginning to lose my edge. To me, the next frontier for personal electronics is wearable technology, and the natural application is self quantification.

But what to quantify? As a hardware hacker and artist, my first foray into QS wearable technology was definitely more for entertainment purposes. Called the Narcisystem, it was a biosensor suit featuring sensors for pulse, heading, EEG, pedometry, and breath alcohol level. I used the output of these sensors to drive the lights, sounds, and ambiance at a party venue. Fun, but not really a form of human augmentation.

I have terrible trouble remembering the names and faces of people I meet. Its hard to say which is worse: my face-blindness or my memory for names. I’ll meet someone, shake their hand, and we’ll introduce ourselves. Moments later I realize with panic that I’ve already forgotten their name! And hours later, if they’ve changed clothes, altered their hair, or removed their glasses, I’ll blithely reintroduce myself like we’ve never met. At least I’m not shy!

I’ve always wanted to offload the mental burden of remembering people. When I was in school and I needed to remember something I used flashcards. Why couldn’t that technique work for people too?

Q: What impact has it had? What reactions have you had?

Gradman: Because the Facelogger is a first-stage prototype I am its only user. Has it helped me remember people I meet? You bet it has. I’m amazed by the quality of the Facecards and by how effective they are at jogging my memory. I get the general sense that reviewing Facecards a day or two after meeting someone gives me an opportunity to properly commit someone’s name and face to memory at my own pace … something I simply cannot do “on the fly” as we meet.

There’s another purely psychological effect: because I’m confident that my technology is taking care of remembering for me, I can relax into the conversation. I was never shy about saying “hi” to people before, but I did experience stress over the fact that I immediately forgot their name and face. Now with that interaction captured and searchable, I’m not bothered at all.

I’m sensitive to the ethical concerns with capturing someone on video without their consent. When asked what I’m wearing around my neck—and as you might expect, that happens a lot—I never lie. I explain that I’m wearing a video camera to help me remember people I meet. Invariably, I’m asked “is it recording me now?” I’ve been asked to turn it off, and I always comply. But a surprising number of people tell me they want their own Facelogger. It turns out there’s demand for a device to help remember people’s faces and names!

Some have questioned the legality of wearing a video camera. But there are already cameras trained on us wherever we go. You can buy a video camera hidden in a pen, or a pair of sunglasses. Will our social mores (or our laws) surrounding cameras trail so far behind the technology?

Very few have actually questioned the morality of wearing a video camera. In the age of pervasive social networking we’re living highly examined lives. For anyone with a camera on their mobile phone, its not such a stretch to imagine wearing the camera around their neck.

Also, I’m careful to remove the Facelogger when I’m not likely to meet new people:at home, in a business meeting, etc. I do this because the purpose of this device is not to have a record of every conversation I have.

Q: What makes it different, sets it apart?

Gradman: Life-logging is always something that fascinated me, but I felt that an ever growing cache of unsearchable video of my life would just be a huge burden. Facelogger is an experiment in constrained lifelogging. By only capturing moments that share a particular characteristic and have common features Facelogger allows for a well-defined process of data extraction and collation that address a specific shortcoming.

Gordon Bell, the pioneer of life-logging described his always-on MyLifeBits image recorder as “write-once, read-never.” For me, the decline in storage costs is not sufficient reason to record my entire life on video. Huge amounts of unprocessed video is just video I’ll have to review someday! That’s why I find it so easy to resist the temptation to press the “capture button” more often. Unless I have automatic tools to convert video into a compact searchable representation—in this case, a Facecard representing a person I’ve met—the video just isn’t worth saving.

There are other tools out there designed to help remember names and faces. Evernote recently released Hello, a mobile app to record people. What distinguishes Facelogger is it’s passive form of information capture.

Q: What are you doing next? How do you see Facelogger evolving?

Gradman: Currently, a Facecard only expresses information captured in the 30-second clip. But APIs for face identification are getting really good. Soon the Facelogger will dig through my social network, and connect a Facecard to the social profile of the person it represents.

Next I will passively capture my meals, and use Mechanical Turk to help catalog my meals.

Face logging and food logging are only two well-defined applications of life-logging. I intend to identify others, and make them available as software for anyone wearing a compatible life-logging rig.

Q: Anything else you’d like to say?

Gradman: Face-blindness and poor memory for names are widespread problems! I designed the Facelogger with my own shortcomings in mind, but I’m now examining how I can make these tools more widely available, perhaps as a subscription service.

If you’re interested in updates on this project, have ideas to improve the system, or want to be contacted when the Facelogger service is available for beta-testing, please join the mailing list.

Product: Facelogger
Website: http://www.gradman.com/facelogger
Platform: Currently, iOS.  Coming soon to any HTML5 enabled smartphone.
Price: not yet for sale; to be contacted for beta-testing, please join the mailing list

(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com) 

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Quantified Self Around the World

So there I was the other day sitting in a coffee shop and perusing the various Quantified Self Meet Up groups around the world. I was blown away by the how many people are taking time out of their busy lives to host and attend meetups in their cities. As we’ve mentioned before, Quantified Self has been growing by leaps and bounds, and it is due to our enthusiastic community of experimenters, tool makers and learners. We now have 45 groups around the world sharing their experiences and knowledge! Take a look at the map below to get a glimpse of our worldwide community.


View Quantified Self Meetups in a larger map

If you’ve never been to Meet Up take a look at the map to find one in your area (links to Meet Up groups are included). Is there no group in your city? Well go right ahead and start one! We’ve compiled a great FAQ to get you started and on your way.

Did we miss your group? Add it to the map (it’s public and editable) or just post a comment and we’ll add it to our list.

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K. Thomas Pickard on Restless Legs and Niacin

Thomas Pickard has had Restless Legs Syndrome for the past thirty years, but was only diagnosed ten years ago. Since his diagnosis, he has experimented with drug dosage, had his genome partially sequenced, and started a RLS/Niacin study on Genomera. In the video below, Thomas talks about what he learned about his sleep, blood and genetics, and what his next directions are for reducing his symptoms. (Filmed by the Bay Area QS Show&Tell meetup group.)

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Do You Take Magnesium?

This is a guest post from Ken Snyder of Quantified Self London. Thanks Ken!

I have personally found Magnesium to be a great tool in getting better sleep, although I believe a more common use is to help get more sleep. In any event, I thought it would be interesting to hear from the community IF and HOW they use Magnesium. The survey will only take 5 minutes if you can spare the time (and less than a minute if you’ve not tried Magnesium):

The Magnesium Effect

Ken Snyder
ken@ken.net

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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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