Category: self-experimentation

Unofficial Beer Tasting Winner: Uncommon Brewers

Seth Roberts6 Comments

Last night I went to a beer tasting in San Francisco. I didn’t taste all the beers but of the 15-odd I did taste the best were by Uncommon Brewers — especially their Siamese Twin (“the floral notes of lemongrass and sharper bite of kaffir lime blend with the deep malt”) and Baltic Porter (“whole licorice root and star anise”).

Five or six years ago I went to a sake-tasting event in San Francisco called “The Joy of Sake”. About 140 sakes. In a few hours I became such a sake connoisseur that the sake I could afford — and used to buy regularly — I now despised. The only sake I now liked was so expensive ($80/bottle) that I never bought another bottle of sake.

 

Health Care Stagnation

Seth Roberts38 Comments

In December, the Los Angeles Times reported — very briefly — that from 2007 to 2008, life expectancy in the United States declined by 0.1 year. It should have been the lead story of every newspaper in the country with the largest possible headlines (“ LESS LIFE“). Did 9/11 reduce life expectancy this much? Of course not. Did World War II? Not in a visible way — American life expectancy rose during World War II. I can’t think any event in the last 100 years that made such a difference to Americans. The decline is even more newsworthy when you realize: 1. It is the continuation of trends. The yearly increase in life expectancy has been dropping for about the last 40 years. 2. Americans spend far more on health care than any other country. Meaning vast resources have been available to translate new discoveries into practice. 3. Americans spend far more on health research than any other country and should be the first to benefit from new discoveries.

Maybe I’m biased (because my research is health-related) but I think this is the biggest event of our time. It is the Industrial Revolution in reverse — progress grinding to a halt. For no obvious reason, just as the Industrial Revolution had no obvious reason. Health researchers have been given billions of dollars to improve our health, the whole system has been given tens of billions of dollars, and the result is … nothing. Worse than nothing.

No journalist, with the exception of Gary Taubes, seems the least bit aware of this. It is a difficult story to cover, true. But several journalists, such as health writers for The New Yorker (Atul Gawande, Michael Specter, and Jerome Groopman) are perfectly capable of covering it. They haven’t. With a few exceptions, they write about progress (e.g., Peter Provonost’s checklists). It is like only reporting instances when Dirk Nowitzki missed a free throw. Each instance is true but the big picture they create — he misses all free throws — is profoundly false.

Among academics, the stagnation has received a tiny amount of attention. In a recent paper (gated), two University of Southern California professors, considering a wider time period, point out that there has been some improvement in how long you live after you get sick, but no improvement in how long you live before getting sick. Here is how the discussion section of their article begins:

There is substantial evidence that we have done little to date [meaning: from the 1960s to the 1990s] to eliminate or delay disease or the physiological changes that are linked to age. For example, the incidence of a first heart attack has remained relatively stable between the 1960s and 1990s and the incidence of some of the most important cancers has been increasing until very recently. Similarly, there have been substantial increases in the incidence of diabetes in the last decades.

Here is my explanation of the paradox of: 1. Enormous and increasing health care costs. 2. Vast amounts spent on research. 3. No better health. Health researchers, such as medical school professors, shape their research to favor expensive treatments, such as expensive drugs. In fact, the best treatments would cost nothing (e.g., the Shangri-La Diet). To make the expensive treatments seem worth studying, they invent utterly false theories and claim to believe them. For an example (research about depression), see The Emperor’s New Drugs by Irving Kirsch. Because health researchers are forced to worship absurd theories, they are incapable of good research. Absence of good research is why there is no progress. The health care supply chain — everyone between you and the research, such as doctors, nurses, drug company employees, hospital employees, alternative medicine practitioners, medical device makers, and so on — is happy with the situation (useless research) because it ensures that little will change and they will continue to get paid. They are the supposed experts — and remain silent.

It is human nature that everyone in the supply chain remains silent. They are protecting their jobs. But the silence of the journalists is The Emperor’s New Clothes writ large. To explain why smart journalists fail to notice the stagnation, I think you have to go back to studies of conformity. When everyone you talk to — people in the supply chain — says black = white (i.e., that progress is being made), you say the same thing.

Why is personal science, the main subject of this blog, important? Because it is a way out of this stagnation.

Albert Einstein: Out-of-Touch Theorist

Seth Roberts19 Comments

Martin Wolf relays what passes for wisdom:

Albert Einstein is reported to have said that insanity consists of doing the same thing over and over again and expecting different results.

Which, if true, shows that Einstein was a theorist.

Call me insane. Based on many years of data collection, I believe scientific progress has a power-law distribution. You sample from this distribution when you collect data. You collect data again and again — “doing the same thing over and over again”. Almost all the data you collect produces little progress; a tiny fraction produces great progress. The secret to scientific progress is doing the same thing over and over — and being wise enough to grasp that the results will vary greatly. (Nassim Taleb understands this.) In the short term, it seems like you are getting nowhere.

I learned this lesson from my sleep research. For ten years I tried various solutions to my problem of early awakening. Nothing worked. All my ideas were wrong. Eventually I got “lucky” but actually I made my own luck by persisting so long.

Once you realize the distribution of progress, you grasp that the secret of success is making the cost per sample as low as possible. Few scientists, in my experience, have figured this out. They prefer expensive experiments because larger grants signal higher status. Won’t fancy equipment tell me more? they rationalize. Grant givers, also failing to understand the basic point, are happy to oblige the status-seekers: Much easier to administer one $200,000 grant than 10 $20,000 grants. And progress slows to a crawl.

More Rita Mae Brown is a more likely source of this saying than Albert Einstein.

Self-Tracking as a Source of Political Power

Seth Roberts2 Comments

The more certain you are the more power you have to convince others and convince yourself. You may want to convince them that change is needed — e.g., that a polluting factory should be shut down or cleaned up. China has a huge problem with industrial pollution, as this report describes. Children are especially at risk.

The danger to those in power posed by self-tracking — in particular, blood tests that measure lead — is shown by this quote from the report:

Even parents who were able to access [lead] testing for their children reported difficulties in obtaining the results of the tests conducted. Many parents in Yunnan and Shaanxi reported that test results from their children’s lead tests were withheld completely. Some parents in Yunnan and Shaanxi told Human Rights Watch that they never saw any test results. Others were allowed to see the results from initial testing but were prevented from seeing the results from follow-up testing.

My daily arithmetic tests (how fast can I do simple arithmetic, such as 3 + 5) have the same purpose as the lead tests: to assess the quality of the environment. If my scores get worse, it may reflect poisoning. Comparison with a blood test for lead highlights strengths and weaknesses of my arithmetic test.

Strengths

1. Sensitive to many things. Can detect any bad influence on the brain, not just lead.

2. Free in the sense that the cost is zero (so long as you have a laptop).

3. Unrestrictable. No one can deny you access.

4. Fast. You get the results immediately.

5. Great sensitivity. You can test yourself as often as you want. The more tests you do the more easily you can detect a change.

6. Variability known. By looking at a graph of your data (score vs. day) you can judge the natural variability — essential for judging the importance of a deviation. With lab tests, the variability is rarely known to the person whose blood was tested or the doctor that reviews the results.

7. Measures what you care about. You care about health. Brain health is part of that. Sure, high levels of lead are bad, but what about low levels? Is there a hormetic effect? The dose-response function isn’t obvious.

Weaknesses

1. Unconventional. A lead test is easier to understand.

2. Unspecific. If a score is bad (= if I get slower) it isn’t clear why. If you have too much lead in your blood the cause is likely to be obvious (e.g., polluting factory, lead in food).

3. Sophistication needed. The arithmetic test is sensitive to hundreds of environmental factors, I’m sure, so identifying the cause of any change inevitably requires sophistication. For example, perhaps you need to control the time of day. Another example is that you need to control/allow/adjust for practice effects.

If the Chinese parents were able to measure their children’s brain functions themselves, they might be far more outraged — and therefore far more powerful.

Assorted Links

Seth Roberts1 Comment

Conway’s Law and Science

Seth Roberts7 Comments

Conway’s Law is the observation that the structure of a product will reflect the structure of the organization that designed it. If the organization has three parts, so will the product. In the original paper (1968), Conway put it like this:

Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure.

Here is an example:

A contract research organization had eight people who were to produce a COBOL and an ALGOL compiler. After some initial estimates of difficulty and time, five people were assigned to the COBOL job and three to the ALGOL job. The resulting COBOL compiler ran in five phases, the ALG0L compiler ran in three.

A consumer — someone outside the organization who uses the product — wants the best design. Conway’s Law implies they are unlikely to get it.

I generalize Conway’s Law like this: It is hard for people with jobs to innovate — for reasons that outsiders know nothing about. Whereas persons without jobs have total freedom. An example is a politician who promises change but fails to deliver. The promises of change are plausible to outsiders (voters) so they elect the politician. However, being outsiders, they barely understand how government works. When the promised changes don’t happen, the voters are “disillusioned”.

To me, the most interesting application of the generalized law is to science. In my experience, people who complain about “bad science”, such as John Ioannides and Ben Goldacre, have the same incomplete view of the world as the “disillusioned” voters. They fail to grasp the constraints involved. They fail to consider that the science they are criticizing may be the best those professional scientists can produce, given the system within which they work. Better critiques would look at the constraints the professional scientists are under, the reasons for those constraints, and how those constraints might be overcome.

“Much research is conducted for reasons other than the pursuit of truth,” writes Ioannidis. Well, yes — people with jobs want to keep them and get promoted. They want to appear high status. That’s not going to change. It’s absolutely true that drug company scientists slant the evidence to favor their company’s drug, as Irving Kirsch explains in The Emperor’s New Drugs. But if you don’t understand what causes depression and you’re trying to produce a new anti-depressant and you want to keep your job . . . things get difficult. The core problem is lack of understanding. Lack of understanding makes innovation difficult. Completely failing to understand this, Ioannidis recommends something that would discourage new ideas: “We must routinely demand robust and extensive external validation—in the form of additional studies—for any report that claims to have found something new.”

Truly “bad science” has little to do with what Ioannides or Goldacre or any quackbuster talks about. Truly bad science is derivative science, science that fails to find new answers to major questions, such as the cause of obesity. Failure of innovation isn’t shown by any one study. Given the rarity of innovation, it is unwise to expect much of any one study. To see lack of innovation clearly you need to look at the whole distribution of innovation. Whether the system is working well or poorly, I think the distribution of innovation resembles a power law: most studies produce little progress, a tiny number produce large progress. The slope of the distribution is what matters. Bad science = steep downward slope. With bad science, even the most fruitful studies produce only small amounts of innovation.

Just as outsiders expect too much from professionals, they fail to grasp the innovative power of non-professionals. Mendel was not a professional scientist. Darwin was not a professional scientist. Einstein did his best work while a patent clerk. John Snow, the first person to use data (a graph) to learn the cause of an infection, was a doctor. His job had nothing to do with preventing infection. To improve innovation about health (or anything else), we should give more power to non-professionals, as I argued in my talk at the First Quantified Self Conference.

Thanks to Robin Barooah.

 

 

 

 

Andrew Solomon on Right to Death

Seth Roberts6 Comments

From The New Yorker website:

My brother and I had by then been authorized by Willie’s next of kin to make his medical decisions. When we asked to discontinue life support, the hospital began putting up barriers; they did all they could to prevent our doing what Trish, my brother, my father, I, and everyone else who knew and cared about Willie agreed he’d have wanted. . . . . Hospital officials repeatedly accused me of murdering him, and wildly misrepresented New York State law relevant to his case. We had, with his biological family, the legal right to decide on his behalf, and having to duke it out with these doctors exacted a great cost we should not have had to pay.

Ugh. I am sorry Andrew did not name the hospital.

Questions About One-Legged Standing and Sleep

Seth Roberts5 Comments

Rajiv Mehta asked some questions about using one-legged standing to improve sleep. I do three sets of two (left leg, right leg) each day.

Q. How do you spread out your three sets (have you found some minimum time between sets, say 3 hours)?

A. I make sure there’s at least 4 hours between sets. The effect was weaker with only 2 hours between sets. The time of day doesn’t matter but for convenience I usually do one set in the morning, another set in the afternoon, and a third set in the evening.

Q. They say exercise before bed is not a good idea. Do you make sure your last set is at least X hours before bed?

A. No. If anything this particular exercise will make you more sleepy, not less.

Using the Tonic app for this.

 


Christine Peterson’s Zeo Research

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Christine Peterson’s poster of her Zeo research was one of the highlights of the QS conference for me, as I said. Here’s why.

The correlation between Sleep Stealer score and time awake. When her Sleep Stealer score was 5 or less, she was awake about an hour during the night. When her score was more than 5, she was awake about two hours — a big difference. There should be a big difference, but you could fail to see it for a thousand reasons. The large difference is a validation of the whole thing — above all, an indication that her Zeo is working correctly.

Even when her Sleep Stealer score is low, she is awake a long time. This means there are major determinants of sleep depth not captured by the Sleep Stealer score. With the right Sleep Stealer score — assuming the correlation reflects cause and effect — she can improve from two hours to one hour (one hour difference) but that leaves one hour. This implies that the determinants of time awake not in the Sleep Stealer score are just as important as those that it contains.

Even when she is at the best level of important factors, she is awake a long time. When she had no drinks, she was awake 56 minutes/night. When other people didn’t disrupt her sleep at all, she was awake 54 minutes/night.

The average wake time for women 50-59 is half an hour. That’s a lot of lost time, day after day, night after night. Note however that the data is from Zeo users, who may have worse sleep than average.

It only took three months to collect the data. This isn’t on the poster. Yet this is a solid contribution, in the sense that I learned from it. With perhaps nine months of data and better data analysis, it might be publishable. The main point such a paper would presumably make is that even when you do everything right (Sleep Stealer score = 0) you’re still awake a lot. This point is nowhere in the sleep literature.

Christine, if you would like to sleep better I suggest:

  1. Don’t eat breakfast until at least three hours after you wake up.
  2. Get at least one hour of sunlight early in the morning — e.g., 6 to 7 am. You can do this by working outside. (I work outside several hours every morning.)
  3. Stand on one leg to exhaustion four or more times per day. (I do it six times/day.) You can do this while reading — it should not reduce your free time.

 

 

 

Highlights of the First Quantified Self Conference

Seth Roberts4 Comments

The First Quantified Self Conference happened last weekend in Mountain View. It resembled a super-duper QS meetup: more talks, more varieties of expression (short talks, long talks, booths, posters, breakout sessions, panels), people from far-flung places, such as Switzerland, and more friends.

Above all, it felt sunny, after a long overcast. Something I’d done most of my life was now enthusiastically being done by many others. Other highlights for me:

Talking with Steve Omohundro, an old friend I hadn’t seen in years. After I saw him on the attendee list, I aimed it at him. After it, he came up and said he really liked it. Mission accomplished :-) . Like so many smart people, he has started to eat paleo.

Meeting John de Souza. I really admire what he has done at Medhelp (“the world’s largest health community”). I like to think that, in the future, the first thing you’ll do when you have a serious health problem will be to contact others who’ve had that problem.

Christine Peterson‘s poster. She measured her sleep with a Zeo for three months. Her poster showed how various things, such as caffeine consumption, correlated with sleep measurements, such as REM time. I believe the most important Zeo measurement is how long you are awake during the night (less is better). Christine’s data showed a strong correlation between her score on Zeo’s Sleep Stealer‘s index (you get points for all sorts of things, such as alcohol consumption, that studies have shown disrupt sleep) and how long she was awake at night. With a high score, she was awake twice as long (about 1.5 hours) as with a low score. This shows the practical value of the Zeo. Assuming that the correlation reflects cause and effect, it’s now clear how she can improve her sleep (reduce her Sleep Stealer score). It also shows that what’s true for other people is true — in the sense of helpful – for her.

The difference between two breakout sessions. Robin Barooah and I ran two breakout sessions about self-experimentation. In the first (many attendees), we talked about 15% of the time. In the second (six attendees), we talked about 5% of the time. In the second, but not the first, it became clear that everyone had something they wanted to talk about. If they could talk about it, they were pleased.

Migraines. I met a woman who used self-experimentation to figure out that her migraine headaches were due to common household chemicals. Doctors had repeatedly told her she had a brain tumor. One doctor had proposed trying twenty-odd medicines one by one until one of them worked.

Speaking advice. Melanie Cornwell, a friend of Gary Wolf’s, gave me advice about my talk. I’m sure her advice will help me in the future.

Mood improvement via sharing. At an excellent mood measurement breakout session run by Margie Morris, I learned how Jon Cousins had tracked his mood for several years and then started to share it with a friend. The sharing had a huge positive effect. He has started a website called Moodscope to help others do this. At the same session Alexandra Carmichael told about sharing mood ratings with someone who, at first, was not an especially good friend but who became her best friend. The sharing improved her mood ratings and curiously their moods become more synchronized.

So I enjoyed the conference on several levels: socially, professionally, and intellectually. Above all, as I said, it was a relief to finally meet others with similar values and goals.