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Tag Archives: qstop
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.
Welcome Robert Lynde!
Running Quantified Self Labs, the organization that supports and coordinates fun QS events and communities around the world, takes funding. We’re very grateful to have generous sponsors that believe in the movement we’re nurturing and want to help make it blossom. And now we’re excited to announce that Robert Lynde is joining Gary, Ernesto, and I, to keep the magic going.
Robert is a long-time runner with a passion for self-tracking, a wonderful heart, and an inspiration to disrupt health care. He’s also the Deputy Director of the MiraiBio Group of Hitachi Solutions America. As our new Sponsorship Facilitator, Robert will be helping to support and recruit sponsors so that we can expand our QS activities worldwide and do some special things around the upcoming conference in September.
Welcome, Robert!
Spark: Visualizing Physical Activity Using Abstract Ambient Art
Chloe Fan has been self-tracking since she was 14 years old and saw the first Harry Potter movie in theaters. She is currently a Ph.D. student at Carnegie Mellon’s Human Computer Interaction Institute. After finding her passion for data visualization and information design for self-tracking tools, she has decided to take a year off grad school to pursue her dreams at full speed in the Bay Area. She is available for consulting or full time positions!
There is a huge increase in the number of personal informatics tools over the last few years that help us track various aspects of our daily lives. The majority of consumer tools use visualizations, often in the form of charts (i.e., column graphs, line graphs, scatterplots), to help users understand the numerical data that they are collecting, and find meaning in their behavioral patterns. Research tools have also used nature metaphors, like a garden or a fish tank, that thrives on physically active users. While promising in motivating behavior change, they can also be punishing when users are inactive (sad fish or wilting flowers).
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I am specifically interested in exploring abstract art as visualization for physical activity. It’s able to present information in an aesthetic and neutral way that is non-judgmental. Reflecting on abstract artwork by Wassily Kandinsky, Piet Mondrian, and Jackson Pollock, I created Spark, a system that uses abstract art in a dynamic ambient display for physical activity.
System Description
Spark is a web application hosted on Google App Engine. The backend is coded in Python, and the frontend coded in HTML, CSS, and JavaScript. It can be viewed on a computer, or more ideally, displayed on a surface in the home (e.g., a tablet computer mounted on the wall, or projected directly onto a wall). The data visualizations are created with Raphaël, a JavaScript vector graphics library, and HTML5 Canvas. Spark uses OAuth and the Fitbit API to continuously pull step count data from the Fitbit tracker and display them using one of 4 current abstract visualizations.
Each visualization is an animation that unfolds as the day progresses. Circles are created based on step counts. The size of the circle represents number of steps, and the color of the circle represents intensity (casual walking, brisk walking, or running).
Spiral
Every five minutes, a circle appears in the middle of Spiral that represents the steps taken during that five-minute period. It pushes previous circles outward in a spiral, so steps taken earlier that day appear at the edge. If no steps are taken in that five-minute period, no circles appear.
Flora
In Flora, rings of color are added around a circle for every five-minute period with step counts. The result is a series of concentric circles showing periods of activity throughout the day, with the final size indicating the total step count for the day.
Bucket
In Bucket, colorful circles fall from the top and fill up the screen to represent steps taken every five-minutes. We found that the use of concentric circles made the visualization more aesthetically pleasing; however, the concentric circles do not yet represent anything meaningful.
Pollock
Inspired by Jackson Pollock, Pollock is the most random of these abstract visualizations. A white line draws randomly across the screen, and when there is activity, the canvas is splattered with color.
Study
I conducted a study with Spark deployed on tablets in 5 homes over 3 weeks, just to see how people interacted with this kind of display. Everyone reacted positively to it, but the most interesting finding was that the 3 younger adults (20+ females) preferred Spiral and Rings because they were looking for specific time and intensity data regarding their gym sessions. The 3 older adults (58-71 years old) preferred Bucket and Pollock because they were interested in daily cumulative totals from walking a lot.
Some of the things people said motivated them to do more activity short-term were the colors, variety of visualizations, and the visual challenge of filling up the screen with colors. I also got lots of good feedback on displaying the data differently, like as a screensaver or hung on a wall like a piece of artwork.
Future Work
There are many planned features for Spark, including:
- Weekly and monthly view showing the final visualization for each day
- Aggregate daily/weekly/monthly statistics
- Adding more charts to compare with the abstract visualizations
- Inclusion of other physical activity properties, such as speed, location, indoor/outdoor activity, and type of activity (i.e., biking vs. swimming). Currently, the Fitbit tracker does not distinguish between activities, so this feature will need data streams from other sensors.
Spark is still in early stages, but if you’d like to check it out with your own Fitbit data, you can sign up at www.sparkvis.com/fitbit/auth. It will connect to your Google account (username data only) to identify you on the Spark site, and you will also need to log in to your Fitbit account to get your Fitbit data (it’s a hassle, but the easiest way I can get it to run right now). Would love to hear your thoughts on improving Spark!
May QS Newsletter
As we wrap up a wonderful April we here at QS Labs wanted to create something to let you all know what’s going with Quantified Self. Thanks to some prompting from our wonderful worldwide meetup organizers we decided to create the QS Newsletter.
Inside you’ll find a variety of information we hope you enjoy:
- A nice article about active moderating from our very own Gary Wolf
- The current May schedule for QS Meetups
- Recent pictures from QS Meetups around the world
- A profile of Ciaran Lyons, the meetup organizer for QS Singapore
- Links to top videos and posts from quantifiedself.com
- 2012 QS Conference information
Download your copy of the May 2012 QS Newsletter here. Copy it, share it, and let us know what you think!
Hugo Campos on Going Vegan in December
Hugo Campos lives with arrhythmia, and is a self-professed data nudist. He decided to do an experiment last December to improve his health and his heart – going vegan and taking beautiful pictures of every single meal he ate to post to a public Flickr set. In the video below, Hugo gives an animated talk about what inspired him, what challenges he faced, and what he learned. Find out if he’s decided to continue eating vegan! (Filmed by the Bay Area QS Show&Tell meetup group.)
Hugo Campos – Going Vegan in December from Gary Wolf on Vimeo.
Gareth MacLeod on Holistic Tracking and Correlations
Gareth MacLeod is a developer/entrepreneur interested in making QS techniques easy to incorporate into daily life. He built an app that sends him text messages to ask about his sleep, mood, romantic encounters, tooth brushing, etc. He then looks for correlations among the different data streams, and even spent 100 hours building a correlation heat map. In the video below, Gareth talks about how to engineer the perfect day, and interesting things he has learned, like if he watches TV before bed, he feels grumpy the next day. (Filmed by the Toronto QS Show&Tell meetup group.)
Gareth MacLeod – Holistic Correlational Tracking from Gary Wolf on Vimeo.
Numbers From Around the Web: Round 6
There is something really magical about taking data and turning it into a compelling visual image. Even though I’ve already written a bit about the importance of making data visual, I am consistently amazed at how data can be made more appealing and informative by creating eye-popping graphics. Today we are devoting this NFATW post to some amazing projects with beautiful data.
Tom MacWright is an engineer for MapBox and Development Seed and spends his time creating and using amazing visual representations of his data. Here are just two of many wonderful projects.
A New Running Map
Tom wasn’t happy with the data visualization he was getting from his Garmin GPS and heart rate watch so he decided to build his own using tools he works with every day. What came out was a really interesting interactive website that visualizes his running routes along with his heart rate. Click on the image above to play around with him data.
He’s also created a unique representation of the same time of running data (GPS + HR) that anyone can play with called Ventricle. Ventricle allows you to plot your own running data if you have .gpx files.
Minute
I’ve had a long standing interest in how I spend my time interacting with my computer. As a long time RescueTime user I’ve gotten used to having something watching my computer use and informing me about my habits. Tom was also interested in his computer use, but wanted something that had less functionality while still giving him information that was important. So, he developed Minute, a keystroke counter and visualization system that constantly records and displays the keystroke frequency over time.
By using a heat map he is able to better understand the pattern of his technology usage. Interestingly, he is also able to make inferences about his sleep and leisure time as he treats them as the inverse of his keystroke time:
Minute is an open-source application hosted on github so if you’re interested in understanding your own computer use or want to contribute to the project go take a look at the source code.
We’ll wrap up today with a quote from Tom’s post on what he learned from developing and using Minute:
Tracking nearly anything you do is alarming and humbling. The aggregates of our actions are lost on us: we can watch hundreds of hours of television and write it off as a small time commitment. How much is too much? It’s hard to make pretty charts without learning something and thinking about what they should look like.
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.
Toolmaker Talk: Vaibhav Bhandari (Enabling Programmable Self with HealthVault)
A few years ago, there was a lot of hoopla about PHRs (Personal Health Records), and the idea that all of one’s health records would be easily accessible in one place. Things haven’t turned out as rosy, and one major player, Google Health, shut down. However, Microsoft continues to persevere with its version, HealthVault, and Vaibhav Bhandari has written a book explaining how self-trackers can take advantage. Is a book a “tool”?! Surely a book that helps you use a tool qualifies for this series.
Q: How do you summarize Enabling Programmable Self with HealthVault? What is it about?
Bhandari: Enabling Programmable Self with HealthVault is a concise book explaining how Microsoft HealthVault can be used for self-tracking and behavior change. It shows how users can enable automatic updates from well-known fitness devices like Fitbit; how they can collect and analyze their health data; and how application developers can help them with mobile or web-based applications.
The book appeals to a broad set of readers from novice health hackers to professional programmers. It walks the reader through showing how they can easily download information from HealthVault in spreadsheets and track and visualize disparate health data to show interesting health trends about themselves. It outlines the details of the powerful data ecosystem of HealthVault and then shows how to write mobile and web applications using HealthVault APIs.
Microsoft HealthVault is the most prominent example of a personally controlled health record. With its open API, flexibility and connections with multiple health care providers and health & fitness devices, it gives people interested in monitoring their own health an unprecedented opportunity to do their own research on their own data. The other part of the title, “Programmable Self” is a term coined by Fred Trotter, and refers to a combination of Quantified Self and Motivational Hacks.
Q: What’s the back story? What led to it?
Bhandari: For the past three and a half years, I had been part of the HealthVault engineering team. I guided partners and developers building HealthVault applications, and curated an open source community around HealthVault and its client libraries. For this I created a lot of content and code examples, and it became clear that a book explaining HealthVault and its client libraries would be helpful to many.
Over the same time period Quantified Self, Personal Informatics and Motivational Hacks have seen an uptrend. During high-school and college I used to track a lot of factors like time, work-outs, and expenses on a daily basis. Through collaborators and colleagues like Fred Trotter I recently got reintroduced to self-tracking. I learned to appreciate the value of tracking and make it more meaningful by associating goals and self experiments and evaluating it in a qualitative context.
I realized these trends very squarely represent the usage scenarios for HealthVault. HealthVault is a great open health platform to aggregate self-quantification data from health & fitness devices and from connected medical institutions via standards like CCD & Blue Button. It does have limitations. There is minimal graphing and statistical capability; however one can export data and use a spreadsheet. And while it has a good input editor for standard data formats, for anything else you must use the programming interface or a spreadsheet.
Q: What impact has your book and HealthVault had for self-trackers? What have you heard from readers and users?
Bhandari: The book was released about a month ago. The feedback I have received in that short time has been quite varied.
One reader noticed a strange correlation between dental visits (data entered automatically through his healthcare provider) and sleep cycle disruption (data entered automatically through Fitbit). Understanding that sleeplessness was caused by anxiety about his frequent dental visits allowed him to curtail the anxiety. Another reader tracking weight, using the Withings scale, and carbohydrate intake and alcohol consumption spotted correlations that has helped him manage his diet to be competitive in national and international triathlons.
In last few weeks I have also received emails from readers who found the book to be a great aid in helping to design clinical trial experiments for graduate research.
Q: What makes the book different, sets it apart?
Bhandari: Currently, Enabling Programmable Self with HealthVault is the only technical book covering Microsoft HealthVault.
Q: What are you doing next? How are you advancing these ideas?
Bhandari: I’m encouraging readers to contribute sharable spreadsheets on the companion website of the book, http://www.enablingprogrammableself.com. One common denominator among health hackers is use of spreadsheets, be it Google spreadsheet or Microsoft Excel. The kind of data being tracked is of long tail nature and no software does a really good job of presenting an interface which can handle and visualize it. Spreadsheets are a useful tool to extend and visualize the varied data involved. Through www.enablingprogrammableself.com, I want readers to be able to share their Health tracking experiences and perhaps create an Open-Source ecosystem of spreadsheets where members of the community can start with a new tracking methodology easily and see some sample data and visualizations of what has worked or not worked for the community members.
Q: Anything else you’d like to say?
Bhandari: Self-quantifiers are mavens of personal informatics, justifying and promoting citizen empowerment with their Healthcare data. We need to promote communities and tools which put the patient in control of their healthcare. Hopefully, Enabling Programmable Self with HealthVault will add a drop to to the ocean by spreading ideas and tools for toolmakers to empower and motivate citizens to be more involved in their day to day health.
Product: Enabling Programmable Self with HealthVault
Website: http://www.enablingprogrammableself.com
Price: $14.99
This is the 14th 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? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at rajivzume@gmail.com.
Thinking About Side Effects of Personal Informatics Systems
Victoria Schwanda Sosik is a PhD student in Information Science at Cornell University. She designs and evaluates technologies that support people towards goals of mental and physical wellbeing. She works with Dan Cosley in the Reimagination Lab.
Personal Informatics systems often deal in domains and utilize data that are just that: personal. These systems use data that we create through our daily activities (such as going for a run with Nike+) and help us review it in a way that encourages reflection and self-knowledge. While systems often have unintended uses and consequences, it is especially important that designers of Personal Informatics systems think about how their systems may be used and impact users, because they are dealing in domains that are closely tied to individuals’ ideas of self (such as weight and body image). We’ve encountered examples of these side effects in our studies of people’s experiences using Personal Informatics systems in health and fitness, interpersonal relationships, and reminiscing.
Overly Negative Feedback Can Discourage Use (and Users)
Tools designed to encourage weight loss and physical activity like Nike+, FitBit, SparkPeople and Wii Fit strive to help users reach their goals by tracking data such as calories consumed, amount of exercise and/or current weight. One way these tools motivate users is by having them set goals in the system and then displaying the collected data back to the user as positive or negative progress towards their goal. This strategy follows from theory that shows motivation is sustained by people setting small, achievable goals, identifying the difference between their current state and their goal state, and then exerting effort to achieve the goal.
Presenting these data without considering users’ mental states and potential reactions to the data can be harmful, however. One example can be seen in Wii Fit’s Body Test. As part of creating their Wii Fit profile and their system avatar—or Mii—users must complete a Body Test that weighs them, tests their balance and asks them to set a goal. If the user is overweight, they see an animation where their Mii’s girth increases and looks down at its midsection with disbelief, accompanied by an ominous sound effect. The system then displays how far away from a normal BMI the user is. My own first experience with Wii Fit was right after I received it as a Christmas gift. I was at my future in-laws’ house and in front of the whole family when I stepped on the Wii Fit balance board. This was right after my freshman year in college (freshman 15 anyone?) and I wasn’t quite prepared to have my BMI prominently displayed on the 52” screen—needless to say, it wasn’t the most positive first experience with a tool that was supposed to encourage me to be more active.
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In principle, according to theories of motivation this should be valuable and useful feedback that helps people know what they need to do. In practice, however, I was not the only one with such a reaction to the Body Test. Participants in two studies on experiences with Wii Fit rarely returned to track their progress using the Body Test because they often found this display “harsh” and thought “it’s one thing to see your [weight], it’s another thing to see yourself–your [avatar]–as a Stay-Puft Marshmallow man.” If Wii Fit used a more constructive and less degrading visualization, perhaps users would have found the feature motivating and would not have abandoned it after a few weeks as most of our users did in.
Displaying Certain Types of Data Can Create Tunnel Vision
Another potential use of Personal Informatics tools is to help people gain broader self-knowledge about areas of their lives such as their interpersonal relationships. Communication tools like text messaging, email, and Facebook capture interactions that are important to the expression and development of relationships and can be used after the fact to help people make sense of these relationships through visualizations such as Themail shown below (right).
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Perhaps the most commonly used tool that aggregates and displays communication data from a relationship is Facebook’s See Friendship page (shown above, left). See Friendship gathers wall posts and comments, photos, mutual events, liked topics, and friends in common between two Facebook users. While this visualization includes several types of data about a friendship, when we asked people to spend some time reflecting about a friendship using the See Friendship page, we found that the data limited what participants reflected on. Participants often started with the most recent content since See Friendship displays data in reverse chronological order, and didn’t always go far enough back to view content from early on in their friendship. Pictures also tended to receive more attention than text. These pictures reminded people of events and activities that were shared but rarely encouraged reflection on deeper, more personal, and longer-term aspects of a friendship such as its evolution. The overall positive communication that happens on Facebook and the lack of capture of mundane, daily communication further biased reflection towards positive and novel events in a friendship.
Designing Personal Informatics Systems With an Eye Towards Side Effects
Our work suggests that health interventions, and other kinds of Personal Informatics systems, are likely to frequently lead to unintended side effects that occasionally might be harmful to either system use or to the users themselves. We suggest that designers think much more carefully about the potential impacts these systems might have on people’s lives and of the practical and ethical responsibilities that accompany the design of systems that help people know and change themselves.
Beau Gunderson on Online Activity Aggregation
In this talk, Beau Gunderson shares a way to bring all of your disparate data sets, from Facebook to Twitter to Foursquare to Zeo to Fitbit to Runkeeper, together in one collection to be accessed through simple APIs. It’s part of an open source development effort called The Locker Project. The hope is to be able to see new patterns and correlations by bringing these sources of data together. Beau learned some interesting things about himself, and had fun playing with different questions he had about his data. (Filmed by the Seattle QS Show&Tell meetup group.)
Beau Gunderson: Online Activity Aggregation from David Reeves on Vimeo.



































