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Tag Archives: Self-Tracking
The report is called The Social Life of Health Information, and has several interesting findings. Here is an excerpt:
Carol Torgan, a health science strategist, points out that anyone who makes note of their blood pressure, weight, or menstrual cycle could be categorized as a “self-tracker.”10 Add an online component, and you have the ingredients for a social health application or an electronic health record. Our survey finds that 15% of internet users have tracked their weight, diet, or exercise routine online. In addition, 17% of internet users have tracked any other health indicators or symptoms online. Fully 27% of adult internet users say yes to either question.
Wireless users are more likely than other internet users to track health data online. Eighteen percent of wireless users have tracked their weight, diet, or exercise routine online, compared with 9% of internet users who do not have a wireless-enabled laptop or other device. Nineteen percent of wireless users have tracked any other health indicators or symptoms online, compared with 11% of non-wireless internet users.
Separately, looking just at the 85% of adults who own a cell phone, 9% say they have software applications or “apps” on their phones that help them track or manage their health.
Sandy Santra gives a short, passionate talk below on turning Quantified Self data into knowledge. He tracks his migraines, time alone, happiness, meltdowns, panic attacks, and “zombification.” (I’d like to see a follow-up talk on that last one!) He gave the audience a framework for how to turn their own data into knowledge, for human improvement. UPDATE: Sandy has provided a PDF copy of his document, which you can download here. (Filmed at the NY Quantified Self Show&Tell #11 at Parsons The New School for Design.)
I recently received an email from someone having trouble keeping up with her experiment. While there is lots of general advice about discipline and motivation, this got me thinking about how doing personal experiments might differ. Following are a few brief thoughts, but I’d love to hear ways that you keep motivated in your quantified self work.
The desire to get an answer. The main point of an experiment is to get an answer to the initial question. “Will a Paleo diet help me manage my weight?” “Does talking less bring me closer to my kids?” Maybe the principle at play is that experiments which motivate start with great questions.
Built-in progress indicators. If you’ve set up your experiment well, you should have measures that come in regularly enough to keep you interested. This is assuming, of course, that you care about the results, i.e., that you’ve linked data and personal meaning (see below). But unlike other types of projects, maybe we can use the periodic arrival of measurements to stimulate our motivation, such as celebrating when new results appear.
The joy of satisfying a mental itch. Curiosity is a deep human motivation, and experiments have the potential of giving your brain a tasty shift – such as when you are surprised by a result. I especially like when a mental model of mine is challenged by a result. Well, sometimes I like it.
Sharing with like-minded collaborators. At a higher level of motivation, experimenting on yourself is an ideal framework for collaboration with folks who are either 1) interested in your particular topic (e.g., sleeping better or improving your marriage), or 2) are living an experiment-driven life. It is encouraging to get together with people to share your work, and to receive support, feedback, and ideas. Of course it feels good to so the same for them.
Desire to make a change. Finally, if we come back to why we experiment, there should be a strong self-improvement component to what we are tracking. My argument is that, ultimately, it’s not about the data, but about making improvements in ourselves for the purpose of being happier. If the change you are trying is not clearly leading that direction, then it might make sense to drop it and try something more direct. Fortunately, with self-experimentation there is usually something new you can try.
Underlying all of these, however, is the fact that the work of experimentation takes energy. Every step of an experiment’s life-cycle involves effort, from thinking up what you’ll do (creating a useful design), through running the experiment (capturing and tracking data), to making sense of the results (e.g., the “brain sweat” of analysis). Given our crazy-busy lives, there are times when we simply can’t take on another responsibility. So if you find yourself flagging and losing interest in one of your self-experiments, then maybe that is itself some data. Thoughts?
[Image from Steve Harris]
With all the disparate data streams coming out of Quantified Self tools, Stephanie Gerson saw a need to create a tool to bring them all together. In the video below, she presents her project Trackify, a new way for people to find correlations and trends in their data streams. Stephanie also puts out a call for building a Self-Trackers’ Bill of Rights. (Filmed at the NY Quantified Self Show&Tell #11 at Parsons The New School for Design.)
We see a lot of cool things here that people are experimenting with, such as health (sleep, water intake, mood) or productivity (interruptions, hours/day, attention), but we are also trying odder things. My interest is in widening the definition of what could be considered an experiment, so I thought I’d ask, what off-the-wall things have you tracked? I’m also curious to know what kind of support or push back you got from those around you, if they were social experiments. While maybe not terribly odd, here are some of the things I’ve tried:
- Experimented with ways to keep my feet warm while mountain biking in winter (tracked left/right foot comfort).
- Tried changing my thinking around positive events (tracked the event and whether it helped me feel happier to relive it later).
- Played with different ways to prevent “wintry mix” ice buildup on sidewalks (tracked likelihood of falling – with careful testing). (Are you detecting a northern climate?)
- Tested different kinds of one-day contact lenses (tracked ease of insertion, visibility, and comfort).
- Dressed better in public (normally I’m very casual), including wearing a hat (tracked psychological and physical comfort, reactions of others, including – surprise! – special treatment at businesses).
[Image: Office Board by John F. Peto
This is a guest post by Chloe Fan:
Hi! I’m a graduate student at Carnegie Mellon in Human-Computer Interaction, and I’m interested in learning about the barriers that you may encounter while collecting or reflecting on your personal information (e.g., too tedious to collect, information not useful, forgetting to collect).
I’m also interested in learning how long-term users have overcome these barriers. The area of personal informatics/self-tracking is not yet well-studied in academic research, so this research on how people respond and cope with barriers to tracking and reflecting can help us design better tools for tracking personal information.
This study consists of a short 5-10 minute online survey about the personal information you’re collecting, the tool you are using, and any barriers that come up during the collecting and reflecting process. At the end of the survey, you will have the option to enter your contact information if we can follow up with a phone interview. This interview should last about an hour.
If you complete the online survey, you will be entered into a $25 online gift card raffle. If you are chosen for the interview and choose to participate, you will be compensated $10 an hour.
Thank you, and I look forward to hearing about your experiences!
Survey Link: https://www.surveymonkey.com/s/F77P7Q7
Tracking my lifestyle changes and related symptoms on an ongoing basis has proved to be challenging. The severity of my symptoms have never been such that I’ve made detailed note-taking a priority. Instead, I slowly evolved a mental methodology for keeping track of my experiences by focusing on one hypothesis at a time and slowly accumulating what I consider to be informative observations and conclusions.
In practice, I mentally maintain two mental ‘records’:
Seth’s post on Personal Science (especially about “data exhaust” ) got me thinking about big data and the implications for the self-tracking work we do. What evidence is there that big data will infiltrate self-experimenting? Under what conditions will self-tracking move from “small data”, or “data poor” (a few hundred or a few thousand data points) to “big data” or “data rich” (terminology from The Coming Data Deluge)? Let me share some thoughts and get yours.
Big data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing.
This identifies an important problem. While it is natural to throw all our personal data into one big database, there are costs associated with doing so. I don’t mean those associated with capture (clearly we will solve the technical and cultural challenges), but the costs in sensemaking – turning data into actionable wisdom. Let’s put the problem into context and assume the future for personal science looks something like this (help me here):
Here is a guest post by futurist Walter de Brouwer:
Myth: You have to use technology.
Fact: A good guideline is to use a tool that’s appropriate for the job. I know people who get good results using spreadsheets, and paper has some wonder affordances. (Read Malcolm Gladwell’s The Social Life of Paper for a fascinating analysis of air-traffic controllers’ paper-based system.) Then again, with large sets of data, visualization tools are invaluable.
Myth: Not everything can be measured.
Fact: I suggest that, with a little (or maybe a lot) of creativity, you can come up with something you can measure for any experiment. Check out Alex’s post, How To Measure Anything, Even Intangibles. (Bonus: Do you have any that are giving you trouble? Let’s play “stump the blogger!”)
Myth: You have to be a scientist.
Fact: While it probably helps to have a background in science, and better yet one in statistics, we can still do valuable work with rudimentary skills, given you design a strong experiment that can teach you something.