“Moderation in all things,” they say. That may keep a society together, but it’s not the protagonist’s job. Here’s the lead:

I wrote this book in three months while running a startup, launching a hit iPhone app, learning to write 3000 new Chinese words, training to run a four-hour marathon from scratch, learning to skateboard, helping build a successful cognitive testing website, being best man at two weddings, increasing my bench press by 60 lbs, reading 20 books, going skydiving, helping to start the Human Hacker House, learning to throw knives, dropping my 5K time by five minutes, and learning to lucid dream. I did all this while sleeping 8 hours a night, sending 1000 emails, hanging out with 100 people, going on 10 dates, buying groceries, cooking, cleaning, and raising my average happiness from 6.3 to 7.3 / 10. And I wrote this paragraph beforehand–I haven’t edited it since. How did I do all of this? I hacked my motivation.

The Motivation Hacker shows you how to summon extreme amounts of motivation to accomplish anything you can think of. From precommitment to rejection therapy, this is your field guide to getting yourself to want to do everything you always wanted to want to do.

So check it out, let me know what you think, leave a review if you like it or if you don’t, and once you’re done changing your life, see the motivation mission results extras on my website.

The data were too noisy to say for sure with just me in there (let me know if you’d like to contribute your data), but they tell the basic story with weekly averaging of my daily measurements:

Red: +500 for 2 weeks. Blue: +0 for 1 week. Green: -500 for 4 weeks. Blue: +0 for 1 week. Orange: ended experiment, stopped tracking, and just ate whatever for 5 weeks.

I gained weight at about the linear rate expected while eating +500 calories per day, although it’s too noisy to tell. I then quickly returned to my starting weight after the first two weeks of -500. Makes sense, classically speaking. I then stayed there for two more weeks of -500 and the week of maintenance measuring. No further weight loss from my stable weight of 145.

It was really hard to eat +500 calories every day without resorting to more exercise, wheat, or junk food, and the only way I could do it anyway was to eat a lot of dairy. -500 was much easier, but after getting back to 145, it stopped having any effect. This is support for the set point theory. (Weak support, since there aren’t much data.) Given that the data weren’t very good, I decided to stop the experiment before killing myself trying to eat +1000 for two weeks without anticipating being able to learn much.

Then I just started eating whatever, whenever, and I lost another four pounds in five weeks (edit: now seven pounds in six weeks). I don’t know how much I was eating because I wasn’t tracking calories anymore. It was more than during -500, but better matched to my appetite. The only difference was that I got to stop eating dairy in order to meet caloric goals on those days where I was somehow under even on the -500. But yeah, this doesn’t support set point, or anything else.

The BodyMedia armband malfunctioned, the Zeo showed no change (and then started irritating my skin for the first time since I got it 1.5 years ago), the Quantified Mind experiment showed no relationship between calories and performance, my lifts stayed the same, and the bodyfat percentages from Withings and calipers showed variance within measurement error the whole time.

Not a very useful experiment.

So, if I want to continually measure my HRV all day, especially while I’m working at my computer, what do I do? Complain about how inconvenient it is for a year, and then hear from Randy at BodyTrack / Fluxtream about the Polar H7, which can send RR intervals using Bluetooth Low Energy for days at a time on a charge. A day later: he’s written a Mac app to connect to the strap and send the data to BodyTrack / Fluxtream. A day after that: I’ve ported that app to iOS. If you have an iPhone 4S or 5, an iPad 3, or a 5th gen iPod touch, then you can pick up a Polar H7 and some extra batteries, email me (nick@skritter.com) Randy to see about getting a copy of the app, or just check GitHub for the code, and have HRV data forever. I’m shocked at how easy the new Bluetooth protocol makes pairing. You never have to think about it.

And, since the data is on Fluxtream, you can easily compare the data–to the second–with any other data streams you want to hook up, like FitBit, Zeo, BodyMedia, Withings, Google Latitude, Quantified Mind, Flickr, Picasa, Twitter, Mymee, Last.fm, Github, or any custom connector you want to hook up to their API. I can see, for example, how different songs affect my emotional arousal, or how stressed out I am depending on my location. Nice.

It has almost been 3 years since I started doing 24/7 time tracking of my activites. Given the fine granularity (I track in increments of every quarter hour), the data allows for some interesting discoveries of trends.

Case in Point: My Love Life

I was recently inspired to analyze the timing of my physical interactions with 5 people, 2 males and 3 females, with whom I have engaged in non-platonic interactions. [Hat tip to David Wecsler for the catchy title idea "Dating Data"].

More specifically, we’ll look at the time it took from meeting the person for the first time to physically escalating to a point beyond what is considered appropriate for “friends”, more commonly known as the first kiss.

The Timing of First Kisses

Results of the First Kiss Analysis

On average, it took men 7:00 hours of in person interaction before the first kiss, while it took women 7:35 hours. In general, it took on average 7:21 hours before the first kiss. From this small sample, it seems there is actually very little difference between the physical interactions of yours truly with men and women in this analysis.

Given the results of an informal poll done with friends and fellow self-trackers at the Quantified Self conference, there could be much variation of hours before physical escalation occurs comfortably–some reported “less than 5 hours”, while others said “a few months, at least”, and more others with everything in between.

What would you find if you analyzed YOUR dating data?

We would love to hear in the comments!

There was one session that saved me from diabetes cancer in my heart liver bones, though. Chris Kresser gave a talk about Iron Behaving Badly, wherein he warns against oft-undetected high levels of iron from either hemachromatosis or eating too much high-iron food (like liver, shellfish, and red meat). Apparently, unless you’re a woman or a Viking warrior who bleeds prolifically, you can easily get overloaded with iron in your blood, which stays there and gives you a hundred diseases years later. He recommended some non-standard iron blood tests which can detect this. I thought, “I guess I should check that out. I have been chomping the beef and tinned baby clams daily.”

Process for testing for iron overload:

1. Sign up at DirectLabs.com.
2. Buy the Iron Panel test combo. (I paid $69.)
3. Try not to spend forever picking other tests to get at the same time.
4. Make an appointment at your nearest LabCorp. (These are everywhere!)
5. Don’t eat for twelve hours (I could be wrong about needing this), then longboard down to your appointment.

I was in and out in five minutes, and I got my results the next day. Not before DirectLabs called me, though–they wanted to make sure I saw the Alert on my transferrin saturation. The reference interval is 15-55%, higher than 45% is probably bad, and apparently 79% is quick-call-this-guy-before-he-cancers-out high. Other levels were whack, too–but not the serum iron that comes with normal blood tests. I wouldn’t have noticed with regular testing.

Process for treating iron overload:

1. Stop doing whatever it was that overloaded in the first place, like eating a tin of clams a day.
2. Give blood.

I gave blood that week and retested the next week, and my transferrin saturation had dropped to 41%. Phew. I can only give blood every 56 days, though. My baby clam stash is growing dust.

So yeah, if you might be getting too much iron, then test it. I also tested my Vitamin D levels at the same time, which showed that yes, if you expose your Vitamin D pills to light, then they don’t work. I bought a new bottle.

This actually wouldn’t happen.

Yoni proposed an experiment to measure the relationship between caloric intake and weight gain per individual. It’s not easy, but not insanely difficult. I’m going to start doing it on September 1, running through the rest of 2012.

Steps

1. Determine current weight and daily caloric intake X that maintains said weight.
2. Eat Y calories beyond maintenance for 14 days.
3. Eat maintenance calories for 7 days.
4. Average the weight readings for the last 4 days of those 7 to see how much weight was gained or lost.
5. Diet back to the starting weight.
6. Do 1-5 four times, with Y = X+500, X-500, X+1000, and X-1000.

Helpful

• Exercise consistently.
• Measure body fat, too (Withings, calipers).
• Try to maintain similar macronutrient ratios (carbs, fat, protein).
• Track other interesting things, like sleep (Zeo), caloric expenditure (BodyMedia armband), cognitive performance (Quantified Mind), mood, productivity, and blood tests.

Classical theory says that the relationship is supposed to be linear with exercise fixed. That is: f(Y) = c*(Y-X) where f(Y) is the weight change for caloric intake Y compared to maintenance intake X, and c is a scaling constant, like 3500 calories per pound. Energy in minus energy out is weight change, right?

Yoni’s prediction is that the relationship will be: f(Y) = c*max(0, abs(Y-X)-Z) where f(Y) is the weight change for caloric intake Y, abs(Y-X) is the absolute value of the difference between actual intake and maintenance intake, Z is the “metabolic buffering” constant (see later) and c is a scaling constant. The metabolic buffering constant is the degree to which the body adjusts energy expenditure up or down by varying heat production and NEAT (“non-exercise activity thermogenesis”, or more simply, “fidgeting”) in response to changes in dietary intake. His hypothesis is that c and Z are individual-specific, but they have typical values across the population, and that beyond the buffering effect, the relationship will be linear with individual-specific scaling that has low variance across the population (that is, c is nearly constant between people, but is a function of their current bodyweight, or rather, their current intake levels X).

Then there’s the set point theory, which claims that it’s easier to return to the original weight to get away from it. This could apply to either the classical theory or the metabolic buffering hypothesis.

I know of at least one other hypothesis, which is that weight gain would be controlled less by how much one eats and more by how much insulin and maybe ghrelin are released from the foods eaten. I think that this can’t be tested without another experiment that controls for insulin levels, but perhaps you can suggest a way that we could figure this out in this experiment which doesn’t require me to eat like I’m diabetic for four months, which I don’t know how to do accurately.

1. What would be your own predictions?
2. Please criticize this experimental design and propose improvements.
3. Do you want to participate, or know anyone who would?
4. What else would be cool to ask/answer/do?

I’m going to find out for myself what calories do to my weight, and I’ll share the data on this blog. I’m planning to continue eating mostly paleo / Bulletproof / low-ish carb and working out a few times a week.