Topic Archives: Uncategorized
Enjoy these links, articles, and ideas from around the web.
By Whom, For Whom? Science, Startups and the Quantified Self by Whitney Erin Boesel. At our recent conference Whitney and Jakob Eg Larson helped facilitate a breakout session for attendees interested in QS and research. This great write-up explores what transpired during that hour-long session.
Confessions of a Self-Tracker by Michael Painter. A nice short piece about Michael’s experience with the assumed differences between self-trackers who are patients and those who track for athletic performance.
Measuring the Universe. A nice video piece on an Roman Ondak’s instillation at Tate St. Ives – “Through the simple action of measuring oneself, Ondak’s work doesn’t just expand on ideas of space and the universal but also the personal, creating a growing living artwork that questions just what a museum is for.” (via Carol Togan)
The Future of Quantified Self Devices by Aaron Parecki. Aaron, another QS conference attendee, explains his ideas for a possible future of the self-tracking technology ecosystem and how to put the individual at the core.
Consolidate this: Quantified Self edition by Nova Spivack. Is story-telling the future of QS? Nova makes that case that it is.
Stan James finds comfort in his daily habits.So much so, that he found that he kept adding to his routine. He started out by just using physical reminders, but found that tracking tools like Equinimity (meditation) and 750 Words (writing) added a little extra boost. In August of 2012 he started using Lift, a habit tracking mobile and web application. In this great talk, Stan explains what he’s learned from his 14 months of habit tracking. (filmed at the Bay Area QS Meetup).
This Thursday we kick off our fifth Quantified Self Conference. We’ve come a long way since our first conference in 2011 and we can’t wait to open the doors and welcome everyone to a wonderful event.
We’ll be hosting over 400 self-trackers, toolmakers, researchers, and other members of our growing QS community. We’ve planned for two amazing days of talks, breakout discussions, demos, and office hours that are sure to inform, inspire, and encourage. We treat our conference as a “carefully curated unconference.” This means that all of our talks and sessions come from our attendees. We are happy to announce that we have more than 120 separate talks and discussions planned. More than 25% of the attendees will be presenting in some way. This is program made for and by the community. Be sure to check out the program to see what will be going on.
For those of you who are not able to attend be sure to follow along on Twitter by checking out #QS13. We have also set up a Flickr group so that you can check out photos from the conference: Quantified Self Global 2013.
When you move from a small town to a big city you’re faced with a number of interesting challenges. How do you get around? Should you sell your car? When Valerie Aurora moved to San Francisco she faced these common roadblocks, but she also encountered something new: being harassed. In this great talk, filmed at the Bay Area QS Meetup, Valerie explains her rationale for tracking street harassment incidents and what she learned about herself and her new city in the process.
In this wonderful talk from the Bay Area Quantified Self Show&Tell meeting, Ashish Mukharji, author of Run Barefoot, Run Healthy, describes doing three years of continuous happiness tracking, using a single number.
This slide from Mary Meeker’s Internet Trends slide deck (link is to full deck on Slideshare) puts some numbers around what we’ve been noticing among QS Toolmakers: everybody wants to talk APIs.
What would you do if you had access to accurate galvanic skin response (GSR), skin temperature, heat flux, and 3-axis accelerometer data, as well as processed data estimating calorie burn, physical activity levels, steps, and sleep? We are holding a contest over in our QS Forum to provoke good questions that can be answered with our data. And there’s a prize.
Why do this? One of the things I’ve learned moderating Quantified Self show&tell talks over the last five years is that the most interesting and inspiring projects depend first on interesting questions. The data, visualization, and analysis is important, of course. But the meaning rests on having a good question, on personal curiosity and interest.
In conjunction with our upcoming QS Europe Conference in Amsterdam on May 11/12, our friends at BodyMedia have agreed to donate a complete personal SenseWear System (retail price $2,500), a state-of-the-art wearable sensor that allows raw data output. That’s going to be our prize. So if you have good questions, we can supply you with a way to collect the data.
To be clear: we care about your question, not your technical skills. I know that getting this much data about yourself can be intimidating. But data analysis and visualization skills are very high in the QS Community, and we can help you find technical support.
So if you have an interesting question or project that you would like to pursue, please describe it in this thread on the QS Forum. The winning idea will be chosen by QS Labs based on its ability to inspire others in the QS community. We will be having a breakout session at the upcoming conference where we discuss the projects posted to the thread.
Go here to post your proposal:
We want to better serve our community. To that end, we’ve created a short survey to to help us understand how we can use this website to support you and your Quantified Self endeavors. We have some ideas about where this website can go, but we want to hear from our community! Please take a few minutes to let us know what you think about our current website and how it could better serve your needs.
Personal Informatics in Practice: Enabling People to Capture, Manage and Control Information for Lifelong Goals
Bob Kummerfeld is an Associate Professor of Computer Science in the School of Information Technologies at the University of Sydney. Bob carries out research into system support for pervasive user models.
People’s long term, important goals are drivers for using personal informatics tools. For example, if a person’s goal is achieve and maintain good health, this is a driver to capture data such as blood pressure, exercise, activity, sleep and food eaten. Personal informatics tools aim to make it easy for people to capture such information and so that it is available for self-monitoring, so people can see how they are progressing towards their goals. It can also help people decide how to alter their behaviour and then to see if this helps them achieve their goals.
Our research aims to create a personal informatics framework for lifelong goals, by enabling people to have a new form of flexibility and control to:
- set relevant and realistic personal goals;
- link these flexibly to tools that capture relevant personal data;
- monitor their progress towards goals;
- and manage the data over the long term (update, share, delete, archive).
As one might expect, given the importance of goal setting and tracking, there are many goal setting systems, such as HealthMonth, GoalsOnTrack, stickK. While these provide a variety of valuable support for goal setting, they lack support for 2 and 4 above. We aim to address the broad challenges of enabling people to flexibly manage and control their data associated with their long term important goals.
User control over personal data during goal setting:
To help people think about the personal data that will be useful for achieving their goals, we are exploring a rich representation of goals. This should enable people to think more effectively about their goals and the kinds of personal data that could be useful. We draw on theories such as Goal-setting Theory and Social Cognitive Theory which point to the importance of aspects such as specificity, importance and difficulty of the goal, deadlines and feedback about the goal, commitment and self efficacy about being about to complete the goal. So we aim to help people think about these aspects. We explain each of these at the goal setting interface. We suggest personalised default values, and explain the reasons for those recommendations, and allow users to set their own values if they wish.
User control over personal data while linking devices to goals:
Social cognitive theory also indicates that if a person is aware of their potential resources (e.g. monitoring tools, social support) towards achieving goals, they gain insight about their own capabilities. In our system, for example, if a person acquires a step counter, they are advised to set an initial goal of using it to get a baseline, by tracking daily steps walked each day over a week. Suppose this indicates they walk an average 5,000 steps a day. Our system recommends an initial goal of 6,000 steps a day for the next week, explaining that while it is well below the recommended 10,000, it is more likely to be attainable from this person’s baseline. Thus our framework both recommends goals that are likely to be achievable and explains the reasons for the recommendation.
Personal informatics now has many different tools for monitoring health and activity. Users can choose different tools for monitoring different goals. This can create a problem which we call ‘scattered subgoals’. For example, maintaining wellbeing includes several subgoals such as “Walking 10000 steps a day”, “Do at least 30 minutes moderate activity per day”, or “Avoid more than 30 minutes of sitting in front of computer”. Users might use step counters such as Fitbit for monitoring a step goal, mobile applications for logging minutes of activity, or notifiers to remind them if they are in static posture for more than 30 minutes. In most cases, they have to visit different web sites to monitor different goals. This makes it hard to monitor goals. Available goal setting systems have not addressed this issue so far.
Our vision is to make it much more easier for people to monitor their diverse goals because our system enables them to aggregate their personal data for all their goals, extracting it from different systems and keeping it in a single store that the individual controls. Since more and more APIs are becoming available for developing mashups for personal health informatics, we can readily extract such information. The challenge still remains to ensure the person can control this aggregation and then manage the information effectively so that it serves their goals.
User access to aggregated information for goal monitoring:
An important part of our work is to enable people to see several goals together and to log salient notes about them. The example in Figure 1 shows a hypothetical user monitoring three goals:
walking 10k steps/day goal (green graph),
having 5 periods of intense activity per week (red dots)
at least 60 minutes moderate activity daily (blue graph).
The figure illustrates the user noting a quiz that interfered with achieving the goals (just as they noted that they were sick in the previous week). Theories of metacognition indicate the importance of enabling people to for log such salient life events to explain the progress achieved and make sense of long term information and trends.
User control over managing personal data:
Finally, existing systems lack support for people to manage the lifelong personal information. We have identified several important levels of control:
determining which information can be shared with others;
easy ways to remove information, for example when sensor data is wrong (such as when they allowed someone else to use their step counter);
transforming the information into compacted forms, for example, reducing fine-grained sensor data into higher level information about goals, so reducing the amount of information kept, reducing the risk to privacy it creates.
To achieve user control over goal related data, we will design and evaluate interfaces for managing goals and reflection over long term by defining goals; monitoring the social and cognitive information associated with each goal; and reviewing goals. These will enable users to connect sensors and choose the type and frequency of feedback, including e-mail, tweets, desktop notification and ambient displays. The driving design goal of our framework is to ensure user control of personal data.
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.