Tag Archives: Self Experiment
As a long-time meditator, Peter Lewis had a suspicion that meditation could improve brain function, so he conducted a self-experiment and enlisted a few other individuals to help test his hypothesis. By using an arithmetic testing application, a timed meditation app, and an ABA research design he was find out that there was some support for meditation improving his brain function. However, other participant’s results weren’t as supportive. Watch Peter’s talk, presented at the 2013 Quantified Self Europe Conference, to learn more about his process and hear what he learned by conducting this experiment. We also invite you to read Peter’s excellent write up on Seth Robert’s blog: Journal of Personal Science: Effect of Meditation on Math Speed and the great statistical follow-up by our friend Gwern.
My personal science introduced me to a research method I have never seen used in research articles or described in discussions of scientific method. It might be called wait and see. You measure something repeatedly, day after day, with the hope that at some point it will change dramatically and you will be able to determine why. In other words: 1. Measure something repeatedly, day after day. 2. When you notice an outlier, test possible explanations. In most science, random (= unplanned) variation is bad. In an experiment, for example, it makes the effects of the treatment harder to see. Here it is good.
Here are examples where wait and see paid off for me:
1. Acne and benzoyl peroxide. When I was a graduate student, I started counting the number of pimples on my face every morning. One day the count improved. It was two days after I started using benzoyl peroxide more regularly. Until then, I did not think benzoyl peroxide worked well — I started using it more regularly because I had run out of tetracycline (which turned out not to work).
2. Sleep and breakfast. I changed my breakfast from oatmeal to fruit because a student told me he had lost weight eating foods with high water content (such as fruit). I did not lose weight but my sleep suddenly got worse. I started waking up early every morning instead of half the time. From this I figured out that any breakfast, if eaten early, disturbed my sleep.
3. Sleep and standing (twice). I started to stand a lot to see if it would cause weight loss. It didn’t, but I started to sleep better. Later, I discovered by accident that standing on one leg to exhaustion made me sleep better.
4. Brain function and butter. For years I measured how fast I did arithmetic. One day I was a lot faster than usual. It turned out to be due to butter.
5. Brain function and dental amalgam. My brain function, measured by an arithmetic test, improved over several months. I eventually decided that removal of two mercury-containing fillings was the likely cause.
6. Blood sugar and walking. My fasting blood sugar used to be higher than I would like — in the 90s. (Optimal is low 80s.) Even worse, it seemed to be increasing. (Above 100 is “pre-diabetic.”) One day I discovered it was much lower than expected (in the 80s). The previous day I had walked for an hour, which was unusual. I determined it was indeed cause and effect. If I walked an hour per day, my fasting blood sugar was much better.
This method and examples emphasize the point that different scientific methods are good at different things and we need all of them (in contrast to evidence-based medicine advocates who say some types of evidence are “better” than other types — implying one-dimensional evaluation). One thing we want to do is test cause-effect ideas (X causes Y). This method doesn’t do that at all. Experiments do that well, surveys are better than nothing. Another thing we want to do is assess the generality of our cause-effect ideas. This method doesn’t do that at all. Surveys do that well (it is much easier to survey a wide range of people than do an experiment with a wide range of people), multi-person experiments are better than nothing. A third thing we want to do is come up with cause-effect ideas worth testing. Most experiments are a poor way to do this, surveys are better than nothing. This method is especially good for that.
The possibility of such discoveries is a good reason to self-track. Professional scientists almost never use this method. But you can.
With the QS conference around the corner, we’re asking our breakout session leaders to preview their topics here on the blog. There will be many overlapping sessions to choose from, so here’s your chance to learn more about what you want to see, and connect with the leaders in the comments.
Here is Martin Sona, organizer of QS Aachen/Maastricht, on his session “EEG for Self-Experimentation:”
The subject of this breakout session will be experimentation with electrophysiological methods, such as electroencephalography (EEG).
First, a short introduction will shed some light (not a lot though…) on the puzzling phenomenon of ’brainwaves’. Í’ll also tell something about how an EEG works, what the pitfalls of this method can be.
Consequently, you’ll get a short overview of the EEG ‘market’ accessible to enthusiasts and we’ll visualize some data very quickly. There will be some room for questions, but if you have a particular question in mind, feel free to email me and I’ll try to answer it during the session.
There has been an exponential rise in the number of people talking and writing about Quantified Self. Some call it a movement, some call it “the next big thing.” In most, if not all cases, there is a an overwhelming emphasis on the role of technology. Be it new sensor systems, applications, or analytical tools, there is an interesting need to equate Quantified Self with technology. It should come as no surprise then that when people start asking me about Quantified Self one of the first questions I hear is, “What device should I buy?” or “What is the killer app/tool/service for QS?” Maybe this is something you’re asking yourself so let’s talk a little bit about how tools fit into the Quantified Self experience.
Think about the last home improvement project you started. Whether it was fixing a leaky faucet or replacing your carpet you most likely went about your work in a simple step-wise fashion: 1) Identify the problem, 2) Examine possible solutions, 3) Identify the most appropriate solution, 4) Gather the right tools to implement the solution, and then 5) Fix the problem. Tools don’t come in to equation until late in the game. I think the same can be said for your self-tracking or self-experiment. The tool is not the piece that defines what you should be tracking or what experiment you should run. It is merely there to help you gather information that is necessary to produce a new piece of knowledge. And that is the point of this whole endeavor – creating new knowledge. Unfortunately, this is often overlooked because in most cases knowledge isn’t as sexy as a new shiny wireless device.
So if tools are not the end-game here, what is? Let’s take a quick look at the Three Prime Questions:
- What did you do?
- How did you do it?
- What did you learn?
Those three simple questions are great guiding principle for Quantified Self and your own personal self-experimentation. You’ll notice that technology isn’t mentioned in our methodology (what some consider to be a simplified scientific method). In fact, the most important aspect of this methodology, and where we recommend you start your self-experimentation journey, is the last question: What did you learn? Perhaps it is better to phrase it this way, “What do you want to learn?” What is the question that has been nagging you lately. What lifehack, productivity secret, or health tip have you come across and wondered. “Is that true?” or “Will that work for me?” This is where all good experiments start. Whether it is a million dollar experiment in a renowned university lab or a personal experiment that starts in your kitchen, the production of new knowledge starts with a good question.
Only after you’ve identified and refined your question should you begin to look into tools that will help you produce the information that helps you develop the understanding that may lead to an answer. You may even want to develop a methodology or experimental plan before identifying what tools works best for you. In any case, keep in mind that the goal of self-experimentation, of Quantified Self, is to produce and share new knowledge.
Nicholas Manolakos is a programmer and avid reader who has been self-tracking for twenty years. He’s recently been improving his left-right body balance, and can write proficiently with both hands now. In the video below, he talks about many of his experiments, including optimizing cognitive performance, managing anxiety, introducing complexity, dietary experiments and fasting – interestingly, one of the things he discovered is that fasting and giving blood improved his cognitive performance. (Filmed by the Toronto QS Show&Tell meetup group.)
Dr. Mark Drangsholt is a long-time self-tracker who also teaches evidence-based medicine at the University of Washington. He has tracked blood pressure and exercise, atrial fibrillation and what triggers it, deep sleep and sex, diet and body fat. In the video below, Mark shares what he learned about his arrhythmia triggers, and how his self-tracking data helped sway his cardiologist to do a less invasive procedure. He also makes a great case that Quantified Self experiments can be more scientifically valid than many of his colleagues would like to admit. (Filmed by the Seattle QS Show&Tell meetup group.)
Randy Sargent has an hypothesis that eating certain foods, like tomatoes, makes him irritable and anxious. He asked himself, “How can I structure an experiment on myself so that I don’t know whether I’m eating tomatoes or not?” and “How would I go about quantifying my irritability?” In the video below, he explores ways to go about designing the experiment, with some fun input from the audience. (Filmed by the Pittsburgh QS Show&Tell meetup group.)
In the last session of the day, we had a few experimental talks on noticing how food changes physical condition. It was also an interesting series of talks that shows the importance of collecting our own subjective data to back up or refute the other technological data that we might also have access to.
I kicked off the session with my talk “Quantifying My Genetics: Why I have been banned from caffeine”. My colleagues and friends helped me quantify my behavior after one, two, or three cups of coffee by giving my agitation a number from 0-10.
I found out that I’m a slow caffeine metabolizer from my genetic results and it seems like there is a correlation between how caffeine affects me and my genes. My genes are not deterministic, I couldn’t have known how caffeine affects me without making my own independent observations.
On a fun note, the crowd guessed that I had one cup of caffeine today, they were right, I had a cup of tea earlier down in the restaurant, away from the conference.
Next we had Martha Rotter who talked about how she experimented with her diet to solve her skin problems after doctors told her there was not much she could do. She did one allergy test where the results said she was allergic to chicken and soy- but after cutting out both of those foods, she did not see any changes but it gave her the idea to test different food groups.
After her experiment with a chicken and soy-less diet, she tried a few other food groups, eventually hitting on cutting out dairy. Her skin cleared up within two weeks of stopping drinking milk, eating cheese.
I think the take away message from our two sessions this afternoon, don’t be afraid to do your own testing, trust in your results.
Robin Barooah gives an insightful talk below on embodied learning. He used a binary self-tracking system, without keeping any of the data, to train his body to know what foods made him feel energized or lethargic. This awareness helped him to lose 45 pounds over the course of several months, but more importantly, it serves as a model of the power of self-tracking to develop intuition and well-being. (Filmed at the inaugural Quantified Self Silicon Valley meetup hosted by Stanford’s Calming Technologies lab.)