Tag Archives: experiment

Results of The Buttermind Experiment

In August, at a Quantified Self meeting in San Jose, I told how butter apparently improved my brain function. When I started eating a half-stick of butter every day, I suddenly got faster at arithmetic. During the question period, Greg Biggers of genomera.com proposed a study to see if what I’d found was true for other people.

Eri Gentry, also of genomera.com, organized an experiment to measure the effect of butter and coconut oil on arithmetic speed. Forty-five people signed up. The experiment lasted three weeks (October 23-November 12). On each day of the experiment, the participants took an online arithmetic test that resembled mine.

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Discuss: The Quantified Worker

While much of our work here is focused on individual development, there are plenty of circumstances in our professional lives where we can apply the ideas of experimentation. Let me set the stage with some background and ideas, and then I’d love to hear from you on how you widen self-tracking to apply to your occupation.

First, experimentation at work is not new. Frederick Taylor‘s Scientific management popularized applying metrics to factory worker performance in the late 1800s. Later came W. Edwards Deming, who influenced the Japanese Lean manufacturing movement in the 50s, which integrated experimentation, measurement, and continuous improvement. A more contemporary thinker is Thomas Davenport and his ideas on How to Design Smart Business Experiments (an excerpt of a paid article).

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Wandering minds, self-tracking, and citizen science

522517468_8bd0cf0106_m.jpgA reader over at my blog shared the NYT article Wandering Mind Is a Sign of Unhappiness, which reports on research by Killingsworth and Gilbert showing some surprises about distractedness. (My take: First, the least surprising result may be that the world’s happiest activity is reproduction. Second, almost half of the time we are not focused on what we’re doing, and this makes us unhappier.) The timing of this report is perfect given Ian’s recent Self-Tracking Tools post, where he talks about the Track Your Happiness project that the scientists used, along with supporting mobile apps and tools. The study is well-reported, so I’ll riff on it from two perspectives: How do we combine the results with self-experimentation to be happier? and What are the wider implications for citizen science and an experiment-driven life?

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Polyphasic Sleep Experiment at Zeo

There’s a great new post over at the Zeo blog by an experienced polyphasic sleeper – instead of sleeping in one 8 hour chunk, he breaks it up into three segments throughout the day. In his post he shows how he used Zeo to help optimize sleep quality and create a polyphasic schedule that feels better for him than the more common monophasic sleep.

It’s also part of a larger Polyphasic Sleep Experiment involving 11 polyphasic sleepers – Zeo is asking them to document their journey of adapting to polyphasic sleep with writing, sleep data, and video footage. I believe it’s still open to anyone who wants to join, so if you’re interested in going polyphasic, ask Derek at Zeo for more details.
Sweet dreams! 

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Where’s the Universal Self-Tracking Gadget?

one-size-fits-all-cropped.pngA few months ago I was fatigued and decided to try a more rigorous sleep hygiene routine to see if it would help (it did). To make the experiment fun I thought I’d look for a nifty iPhone app to track the data. After a fairly extensive search I noticed that most of the tools were either highly specialized to a domain (e.g., Sleep On It), or more general purpose (e.g., iLogger). This got me wondering about why there isn’t a universal self-tracking gadget, and what one might look like.

Below I sketch some ideas on what such a beast would need to do to support any data-driven effort. I’d love to know if this makes sense to you, and what you think. (Note: I’m excluding memories for life applications such as Gordon Bell’s MyLifeBits.)

Overall functionality

Regardless of the particular domain – sleep, exercise, mood, sex, reading, etc. – is there a set of common tools and sensors that could satisfy the majority of data-driven activities? Overall the goal would be to help answer the types of questions we ask when self-experimenting. That is, to help us discover useful patterns. The kinds of things it would need to “know” include:

  • Physiological state: Physical context like pulse and temperature. (What’s going on in your body?)
  • Mental state: Cognitive context like thinking patterns, mood, and happiness. (What are you thinking? How do you feel?)
  • Location: Spatial context like transitions, surroundings, environment, and activity. (Where are you? What’s going on around you? Where are you going?)
  • Incidents: Temporal context like performing exercise, taking medication, attending an event, or eating. (What did you just do?)
  • People: Social context (Who are you with? What interactions are you having?)

How would it collect these things? I don’t have all the answers, but I’m thinking of three sources: Direct measurement, inference, and self-reporting. The first category, direct measurement, clearly is collected by sensors, and there is exciting progress on this front. See Measuring Vital Signs From 40 Feet Away or NASA Adapts iPhone to Detect Chemicals, for example.

I’m less sure about the second category, inference, but I’m thinking of tools that deduce some of the above, such as “You’re asleep” (zeo), “You’re at work” (Skyhook), “You’re at a party” (iCal), or “You’re around someone interesting” (MeetMoi).

The final category is the most applicable to self-tracking, but also the most problematic. The closest concept I could find was Wikipedia’s Self-report inventory entry, but the gist is there’s a lot we have to report explicitly. Think of anything you’ve tracked in the above contexts and you’ll come up with plenty of examples, such as “I feel great,” “I drank a beer,” or “I just had an argument with my spouse.” This category is problematic because self-reporting is biased, and because it requires manual input (see Gary’s Which is Better: Automated or Manual?).

I think it’s this last category of data capture that’s generally applicable to most self-tracking needs. Putting on my computer science hat, it seems there’s a fixed set of data types that we’d need. The typical ones include itemized lists (mood from 1 to 5 stars, or yes/no), counts (number of push ups), durations (minutes of exercise), number (weight in pounds), and text notes. All would be time-stamped, of course.

Pros and cons of specialized vs. general

Nothing comes for free, so what would be the trade offs of using a general-purpose data capture device? The pros are that there’d be no reinventing the wheel, everyone would know how to use it, and manufacturing economies of scale would be possible. Also, if we assume a open data access API then any site could use the data, enabling custom uses, novel visualizations, and social applications.

For cons, just look at Alex’s roundup series of “vertical” tracking tools: food, location, fitness, and mood. Because these are specialized to their domain they offer benefits like precise language, customized input (such as eCBT Mood), inferred measurements, and inbuilt information such as a food/calorie database.

Workarounds are possible and would be driven by an experimental design perspective. Self-trackers would set up their experiments by specifying types of measurements, units, frequency of capture (including reminders), and measurement groupings. (An example of the latter is needing to capture a set of daily mood chart data in one shot, like exercise, medications, menses, energy, and agitation level.) By making the gadget’s UI “skinnable” we could generate interfaces automatically for each experiment.

Usage characteristics (or Why your phone should be a Tricorder)

proto-typeTricorder-small.pngSo what would the thing actually look like? In addition to the physical sensors, there are characteristics required for a universal data-tracker to be usable. What comes to mind are ubiquitous availability, rapid manual entry, and notifications to the senses (“What’s that smell? Oh, it’s time to check if I’m procrastinating.”)

Fortunately we have a classic model to start with – the venerable Star Trek Tricorder. It was portable, had powerful recording and analysis capabilities, and could measure things like environmental make-up, life forms, and power sources. Combining the general-purpose and medical variants into your cell phone (the de facto does-it-all device), and adding additional sensors and controls (real buttons, please – much faster than touch screens), wouldn’t we have something that self-trackers would love?

A catalyst for citizen science?

Inspired by Kevin’s conclusion in A Web Page For Every Species, I wonder if having a universal device for self-experimentation could launch self-tracking for all.

As he puts it,

When anyone can buy a hand held species identifier, an amazing transformation will take place: everyone will become a taxonomist.

Could this be true for individual experimentation? Would everyone become a personal scientist? It’s exciting to imagine this kicking off a widespread movement to fulfill the promise of citizen science and social self-improvement. What would be the result, and how might that change how we interact with ourselves, each other, and the world?

What do you think?

  • Is such a gadget possible?
  • Would it apply to most self-tracking apps, or would it be too general?
  • Do you use a general purpose app? How has it worked for you?
  • Do you see it drawing people into the experiment-driven life?

[Images from Ralph Aichinger and TK560]

(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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What if you don’t like the data?

2677824178_b876aaa98b_m.jpgIn collecting data about ourselves we naturally encounter information that is unpleasant or unwelcome. This can range in scope from the prosaic (“I’m not losing weight fast enough”) to the the profound (“I just found out I have cancer.”)

While this is a pervasive aspect of being human – resolving the conflict between What Is and What Ought To Be (apologies to David Hume) – I want to play with going beyond the general case of dealing with bad news, such as the oft-mentioned Kübler-Ross model (Shock, Denial, Anger, Bargaining, Depression, Testing, and Acceptance). I wonder if we can get guidance and some measure of equanimity by framing the data from a scientific mindset. Let’s see.

Objectivity

A hallmark of science is striving to view results objectively – what I call a “healthy sense of detachment.” I’m humbled by how Victor Frankl puts it in his book Man’s Search for Meaning:

Cold curiosity predominated even in Auschwitz, somehow detaching the mind from its surroundings, which came to be regarded with a kind of objectivity.

This is not to say you don’t acknowledge or experience your emotions; far from it. In fact, your reactions and feelings are themselves data. But it helps to think of them as playing a separate role.

The act of tracking

We can draw comfort from keeping an experimenter’s journal, which is your record of not just the data but of your experimentation process itself. While this is a common tool in the self-help literature, our take here is inspired by what Jim Collins termed a bug called Jim – the idea of studying and gaining insights from the odd and fascinating creatures that we are. This further reinforces objectivity.

Rational analysis

Doing an analysis of the data can activate the rational mind, rather than being mired in reactive mode from the bad news. To do this you might ask,

  • What was my theory going in?
  • Is the data correct?
  • What is it telling me?
  • Is my interpretation valid?
  • What are my options moving forward?

Action through exploration

One of the two things we can control – our thinking and behavior – acting on the data is empowering. If we can keep our curiosity intact, there is always more to discover. Here are some possible steps to follow:

  1. Classify it as a problem.
  2. Give it a label.
  3. Examine the situation.
  4. Plan solutions.
  5. Execute them.

Control and acceptance

Constraints simplify. In our model of the situation the data describe, we have a finite set of things we can do. As students of self-quantification it is logical to identify realms of control, and to take some comfort from concrete reality – that is, facing the brutal facts. I suppose this is my attempt to make a secular version of the saccharine Serenity Prayer. This perspective doesn’t mean you have to be passive, though. Remember that self-tracking is about the power of discovery.

Collaboration

Finally, rather than the cold, solitary scientist portrayed on TV in a sterile white lab coat, in reality the best science is done in collaboration with others. Soliciting the help of friends, family, and fellow experimenters can enable identifying biases, fleshing out our analysis, and importantly, giving emotional support. This makes it so that it’s not just you and the data. I love Lewis Thomas’s observation that he could tell when something important was going on in an experimental lab by the laughter. And being able to laugh in the face of nasty data – that’s a true gift.

[Image from Okinawa Soba]

(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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Does QS Need An Ethical Review Board?

I am a strong supporter of self experimentation and citizen science, particularly when it comes to health (full disclosure: I’m CoFounder of CureTogether.com). Since our bodies all differ to varying degrees, we need to experiment with foods, lifestyles and medications to find out what will work best for each of us. And pooling our individual data can guide us in choosing intelligently, rather than randomly, which experiments will have the highest chance of yielding answers that will help us.
It will come as no surprise that I’m a great fan of fellow QS member Seth Roberts – a modern pioneer and champion of self-experimentation. At a recent QS Meetup, Seth drew lots of attention from the crowd when he discussed the results of his Butter-Arithmetic experiment. In fact, there was so much interest, that some people decided they wanted to run the same experiment on themselves and pool the data to see if they could replicate his results. This led to Eri Gentry’s Butter-Mind… and Coconut-Mind Study, in which she outlines a good, scientific protocol and writes: “I am currently looking for Butter Mind participants.”
And this is when I became concerned.
In a subtle shift, we went from one person reporting on an experiment he ran on his own body, to a group of people deciding they want to try the same thing, to a public call for participants.
Some would argue that such a call qualifies as an advertisement for a human interventional study, which creates ethical, if not legal, responsibility to establish proper oversight. Specifically, it would require assessment and disclosure of any potential risks of participating and verification that all participants have given voluntary and fully informed consent.
Personally, I think the Butter-Mind experiment is quite safe, and most members of the QS community are likely sufficiently sophisticated to be fully aware of whatever risks it may present. But some might reasonably challenge this, particularly for certain potential participants, and especially when the details of the study are communicated to a wider audience. We already had a cardiologist express concern to Seth about the risks to cardiovascular health of increasing saturated fat consumption.
Most importantly, it is not up to the designers of a study to make the determination of whether research is ethical, whether potential risks and benefits have been properly communicated, and whether informed consent is sufficient. This is the job of an ethical review board.
As the QS movement ventures from simple self-tracking to more sophisticated social experimentation, which offers compelling scientific rewards, there are a couple of options for proceeding.
If there are going to be public calls for participation, then I would strongly urge the QS community to assemble its own ethical review board, according to federal regulations, and to review all studies that in any way seek to actively recruit others to participate.
If we alternatively decide this would pose too great a burden on us self-experimenters, then we need to figure out how to help people with similar interests come together and share data, without anyone “advertising” their study such that it binds us to play by the same rules that were established long ago for pharmaceutical companies.
I am not at all an expert in this, but I think it’s an important distinction that we need to understand and develop rules against.
It would be truly tragic if the nascent QS movement, and its promise for social benefit, became overburdened with regulatory oversight for failure of its pioneers to take appropriate safety precautions. The best way to avoid this is to demonstrate that we have considered the ethical issues and can responsibly regulate ourselves.
For those who might think this is excessive, consider what might happen if, in some future experiment, someone who was not fully informed of potential risks ends up seriously harming themselves.

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Eri Gentry on Butter Mind/Coconut Mind

For those of you following our first Quantified Self group experiment, here is Eri Gentry telling the Bay Area QS Show&Tell meetup about the Butter Mind/Coconut Mind experiment. The question she wants to answer is, does eating 3 tbsp. of butter or coconut oil a day improve math scores? Hear more directly from Eri below.

Eri Gentry – Butter Mind/Coconut Mind from Gary Wolf on Vimeo.

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Is there a data-driven personality?

3841160835_2357897a19_m.jpgLet’s admit it. People who do stuff are more interesting than those who don’t. Naturally we’re biased as Self-Quantifiers, but don’t you love running into folks at gatherings who have surprises and results to share about themselves, gained from experimentation and tasty data? It’s stimulating to hear about an insight (“I eat less when I’m happy”), a problem they’re getting a handle on (“I’m seeing if exercise helps my mood”), or a delightful surprise (“I’ll be darned – I’m smarter when I eat butter.”)

A meta question I’m curious about is whether we can quantify the self-quantifier. That is, can we find a personality type that’s common to all of us who experiment on ourselves? Let’s play with it by looking at a few possible attributes.

  • The insatiably curious. If any of these dimensions are universally applicable, I’d guess it’s the trait that got the species to where it is now – the urge to answer innate questions like “Why did that happen?” or “What if I tried…?” Can there really be anyone who isn’t curious?
  • Gadget lovers, early adopters. There’s no question that the explosion of self-tracking widgets is exciting. Electronics for measuring sleep, exercise, even power consumption provide motivation through novelty, and ease the tracking burden through automation. A little test: Anyone using low-tech tools? Graph paper and lab notebooks for example?
  • Risk takers. Collecting data means trying new things, and as a species change is hard. In my case, some of the experiments I try out can feel pretty scary. In your life, how much of a stretch is it for you to do your experiments?
  • Fans of Occam’s razor. Experimentation is a function of the scientific method, which requires a rational “prove it to me” mindset. Can we be motivated to collect data about ourselves yet not be skeptical?
  • Problem solvers. Often our foray into experimentation is driven by a problem such as a major health concern. (There are over 600 of them at Alex’s CureTogether.) I wonder if motivation to solve a particular situation is at right angles to a general experimental sense. Or maybe it’s the other way around – those who work actively to address a problem are by definition self-experimenters.
  • Tireless self-improvers. As Gary pointed out in his New York Times piece, we track data ultimately to peel back the layers of our behaviors: “The goal isn’t to figure out something about human beings generally but to discover something about yourself.” There’s probably a set of folks who are happy with themselves the way they are, but I don’t think they congregate here. Then again, I always appreciate when someone chimes in and questions our movement.
  • Thrill-seekers. If it’s true that we have built-in novelty detectors, are we more likely to try things because results are more stimulating? I’d argue that, because of our curious nature, experimenting feels good. In your case, what kind of jolt do you get from discoveries?
  • Willing to change. What’s the point of thinking up things to try, doing them, and then capturing and analyzing results if we don’t make a change, either in our thinking or behavior? I don’t mean that change is always the goal (I’m a firm believer that observation leads to awareness, which leads to change), but without change is this work simply waste? Maybe there are stages, starting with “data-curious?”

What do you think? Is it possible to define useful characteristics that capture the data-driven personality? Do any describe you? Which ones would you add or remove?

[image from x-ray delta one]

(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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