Tag Archives: personal science

QS Access: Ian Eslick on Personal Experimentation

“Science is really about repeatability, about process, about discipline, about characterization, about controlling noise, and there are lot of different mechanisms that we can pull together to tell a story or inform a decision.”- Ian Eslick

This past April we were lucky to host a meeting of researchers, toolmakers, science funders, and government representatives for our first Quantified Self Public Health Symposium. This one-day meeting, and the work leading up to it, helped to shape our thoughts and ideas around what data access means and how it can be used to shape personal and public health. Access can take on a variety of different meanings from being able to obtain a copy of your data, to being able to contribute to and use public data sets. But access doesn’t always have to deal with the transfer of bytes of information. What about access to process, people, and ideas?

At that 2014 Quantified Self Public Health Symposium we were happy to have Ian Eslick join us and give a short talk about personal data and the scientific process. Access to the methods of science and the scientific process is an important piece of the puzzle, especially as personal data become easily captured and more readily understood. Too often, the world of science and research is help up on a pedestal, out of reach for individuals struggling to understand themselves. In this talk, Ian touches on his personal journey of self-experimentation and how access to the “tools of science” can be highly impactful, especially for those battling chronic conditions.

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Technology Review Explores the Self-Tracking Movement

Emily Singer, a journalist with MIT’s Technology Review, has an extensive series of articles and interviews on “The Measured Life“. She was at the Quantified Self Conference a month ago, seems to have talked with everyone, and has since been writing up a storm.

The July issue of the magazine has a cool cover, the featured article is on The Measured Life, and there are highlights of many of the popular Tools for Quantifying Yourself.

There’s much, much more on the web.

Emily’s been tracking her own life, and reports on her overall experience, on physical movement, on sleep, and on blood pressure. Also very interesting are the failures, and technology troubles.

There are also video interviews. Kyle Machulis describes his efforts to hack tracking devices so everyone can access their own data. David Marvit talks about Fujitsu’s Sprout project and the importance of obtaining biometrics in real-world conditions. And Rajiv Mehta talks about the potential for personal science to make a significant impact on healthcare and medical science, and demos Tonic.

And there are posts on social networking and games in self-tracking technologies, on astronauts measuring sleep, a physician’s perspective, the new Health Graph effort, and a wristwatch that continuously monitors blood pressure.


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What makes a successful personal experiment?

As I continue trying to stretch the concept of experiment so that a wide audience understands applying a scientific method to life, I struggle with defining success. While the trite “You can always learn something” is true, I think we need more detail. At heart is the tension between the nature of experimentation’s trial-and-error process (I prefer the term Edisonian approach) – which means outcomes are unpredictable – and our need to feel satisfaction with our work. Here are a few thoughts.

Skillful discovery. Rather than being attached to a particular outcome, which we have limited control over, I’ve found it’s better to focus on becoming an expert discoverer and mastering the process of experimentation. Because you have complete control over what you observe and what you make of it, you are guaranteed success. Fortunately, there’s always room to develop your investigatory skills.

Fixing the game. At first it might seem contrived, but carefully choosing what you measure can help implement a scientific perspective on success. For example, instead of framing a diet experiment as “Did I lose weight?,” it is more productive to ask “How did my weight change?” The former is a binary measure (losing weight = success, not losing = failure) and one that you don’t necessarily have control over. After all, you are trying an experiment for the very reason that you don’t know how it will work out. The latter phrasing is better because it activates your curiosity and gives you some objectivity, what I call a “healthy sense of detachment.”

Improving models. As essentially irrational creatures, we run the risk of not questioning what we know. Updating our mental models of people, situations, and the world helps us to be more open to improvements. And the leading edge of that is the conflict between expectation (predicted outcome) and reality (actual results, AKA data). The quantified way to work that is by explicitly capturing our assumptions, testing them, taking in the results, and adjusting our thinking as necessary. This also leads to better predictions; from The Differences Between Innovation and Cooking Chili:

Of course, all of the experimental rigor imaginable cannot guarantee success. But it does guarantee that innovators learn as quickly as possible. Here, “learn” means something specific. It means making better predictions. As predictions get better, decisions get better, and you either fail early and cheap (a good outcome!) or you zero in quickly on something that works.

Getting answers. Another way to guarantee success is by going into an experiment with clearly formulated questions that your results will answer. Structured correctly, you know you will get answers to them. I think of it as regardless of what happens, you have found something out. (Hmm – maybe thinking of the process as active discovery is a richer concept than the generic “you learned something.”)

Designing for surprise. If the product of your experiment was not very surprising, then maybe you should question your choice of what you tried. Exciting experiments probe the unknown, which ideally means novelty is in store. Fill in the blank: “If you’re not surprised at the end of your experiment, then __.”

Zeroing in. Because we usually dream up experiments with a goal in mind, chances are we come out the other end having moved some amount in the direction of attaining that goal. Progress is a success, so give yourself a pat on the back.

Taking action. Finally, each experiment is a manifestation of personal empowerment, which is a major success factor in life. While health comes to mind (do difficult patients have better results?), I think generally the more we take charge of our lives, the closer we get to happiness.

What do you think?


[Image from lincolnblues]

(Matt is a terminally-curious ex-NASA engineer and avid self-experimenter. His projects include developing the Think, Try, Learn philosophy, creating the Edison experimenter’s journal, and writing at his blog, The Experiment-Driven Life. Give him a holler at matt@matthewcornell.org)

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Personal Development, Self-Experiments, and the Future of Search

We experiment on ourselves and track the results to improve the way we work, our health, and our personal lives. This rational approach is essential because there are few guarantees that what works for others will work for us. Take the category of sleep, for example. Of the hundreds of tinctures and techniques available, clearly not all help everyone, or there would be exactly one title in the sleep section of your bookstore, called “Sleep,” and no one could argue about its effectiveness. Treating these improvements experimentally, however, requires a major shift in thinking.

But being human isn’t that simple. There are variables and confounding factors that mean you have to take matters actively into your hands if you want to really know what’s personally effectual. That’s why what we do here is so exciting. Instead of accepting common sense, we take a “prove it to me” approach and work to find out for ourselves. Operating from this basis, rather than faith, is more effective in the long run. (It’s why we use science to understand the world, rather than astrology or phrenology, for example. Just look at what we’ve accomplished.)

As I tried to say in Making citizen scientists, this is heralding a move from citizens-as-helpers to true citizen scientists – people who get genuinely curious about something and decide to test things out for themselves, rather than simply trusting what others say will work. If we expand that vision five or ten years in the future, I think there could be a major shift in how we search for ways to improve ourselves, and that’s what I want to share here.

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Announcing: Why Personal Science Really Matters

T minus 25 days until the Quantified Self Conference, and we have another exciting announcement. Seth Roberts will be giving a keynote on “Why Personal Science Really Matters.”

Seth Roberts’ pioneering work in self-tracking and self-experimentation has led to discoveries about diet, cognition, mood, and sleep. His use of daily measurements of basic activities as a baseline for personal experiments has inspired many in the Quantified Self movement.

Here, he will talk about personal science, and why it matters. Personal science is science done to help yourself. It is especially important now because conventional health care has stagnated badly. Personal science, on the other hand, has not stagnated.

Roberts will give examples of how persons trying to help themselves have found highly useful new ways of dealing with common health problems. He will discuss what we can learn from successful examples of personal science and why our own experiments are more useful and valid than we might think.

Thanks Seth, looking forward to hearing what you have to say at the conference!

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Seth Roberts on Personal Science

In the IEEE Spectrum, Paul McFedries, the author of Word Spy, writes about new words generated by new kinds of science made possible by cheap computing.

Perhaps the biggest data set of all is the collection of actions, choices, and preferences that each person performs throughout the day, which is called his or her data exhaust. Using such data for scientific purposes is called citizen science. This is noisy data in that most of it is irrelevant or even misleading, but there are ways to cull signal.

That’s not my understanding of what citizen science means. I’ve seen it used when non-scientists (”citizens”) help professional scientists. The Wikipedia definition is

projects or ongoing program of scientific work in which individual volunteers or networks of volunteers, many of whom may have no specific scientific training, perform or manage research-related tasks such as observation, measurement or computation

Bird-watching, for example.

My self-experimentation is not citizen science. I am not doing it to help a professional scientist nor as part of a project. I do it to help myself — in contrast to professional science, which is a job. Almost all self-experimentation by professional scientists and doctors has been done as part of their job.

So let me coin a term that describes what I do: personal science. Science done to help the person doing it.

I believe personal science will grow enormously, for several reasons:

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