Why Self-Track? The Possibility of Hard-to-Explain Change

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 breakfastI 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.

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11 Responses to Why Self-Track? The Possibility of Hard-to-Explain Change

  1. Steven Jonas says:

    A related advantage of continuous tracking is that it allows you to have a baseline already established, so that if you have an idea for an experiment or want to try something out that you’ve read about, you already have the data there for the “normal” condition and can jump right into the experimental variable. I have found that I’m much more likely to try something out if I can do it right away. I assume that I’m like many others in that I find it hard to wait a week to establish a baseline before testing something.

  2. Pingback: This week in lifelogging: the medici effect and better brains with butter | Memoto Blog

  3. Some interesting QS actions we could track together to see if they work for others too: https://didthis.com/tag/quantifiedself (includes 2 of Seth Roberts invention: pinkfloydposition & Buttermind)

  4. k says:

    Peer reviewers do not accept this method of generating hypotheses, so you seldom see it published in the journals. However social scientists frequently generate research ideas from similar, if less formal, self-observation. They sometimes sheepishly refer to it as “me-search.”

  5. Derek C says:

    But HOW do you track all that data? Today, with a smartphone, it seems easier, but even then I’d like something akin to a master spreadsheet that I can store it all in.

    Seth: what format are you tracking all that data in?

    Of course, now that I type that, I’m worried the answer is “pen and paper.” :(

  6. Thanks for this post, it’s greatly dampened my interest in self-tracking by illustrating a problem I hadn’t thought of, though it seems obvious now.

  7. Jeff Edwards says:

    One thing to keep in mind though is that this isn’t truly the scientific method at work for all of these effects. While 1-4 and even 6 are plausible and you can test going on and off of them, the mercury amalgam fillings stuff has been thoroughly debunked in the past studies comparing them to resin fillings (which I actually prefer simply because they’re closer in hardness and color to my teeth). Also, the removal of mercury amalgam fillings can actually expose you to more mercury than just leaving them in. The issue is that correlation may imply causation, but is not necessarily indicative of other factors.

    Links to articles containing commentary and references:




  8. Jeff Edwards says:

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  9. Dave says:

    This is less “scientific method” than you’d like to believe. You’d need double-blind studies, you’d need to isolate variables much more strictly than you are, and so on.

    It’s cool and all, but you’re not always doing what you think you are.

  10. Mark Lapierre says:

    Just for interest’s sake, there are a couple of similar ways in which scientists actually do this sort of thing.

    The first is frowned upon. It’s where you just gather a bunch of data and look for something, anything significant. It’s often referred to as a “fishing expedition”. Of the many reasons it’s frowned upon by scientists, perhaps the one is least important to self-tracking is that it’s an incredibly inefficient use of time. There’s always something else you could be working on. But for self-tracking your time is yours and yours alone, so if you want to collect data in the hope that it might one day be useful, it shouldn’t matter to anyone else. But as professional scientists do, so can we; generate hypotheses first based on available information, in particular ruling out possibilities on the basis of existing data.

    The other method is to use data that other people have collected for a different purpose. For example, a massive cancer study could be conducted and it would produce huge amounts of data over many years. Some time later someone else might have a research question about asthma that that wealth of data could help answer because asthma was one of the other medical conditions that the first researchers asked their participants about. I’m not sure if that would be of any help to self-trackers, since no one else should be collecting significant amounts of personal data. But one example that comes to mind is search/browsing history, e.g., using your search/browsing history to test hypotheses about work vs. procrastination habits.

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