The Mother of all Self Tracking

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I haven’t posted in a while and I am due to give my February update.  But while you are waiting for that, here is an amazing analysis of self-data, collected over 20 years, by  Stephen Wolfram, famous mathemetician and designer of Mathematica, and the Wolfram|Alpha ‘knowledge engine’.

Take a look – it’s pretty amazing:   Stephen Wolfram: The Personal Analytics of My Life

Here is a plot of all of his keystrokes, over 100 million of them (!) over 10 years.


Unfortunately, he doesnt talk about how he collected all of this.  Back in 1990 when he started collecting data, computers were very different beasts.  It is amazing that he managed to collect this data over different eras and different technologies.  Awesome.


(LDL) Particle Man


I went to the doctor last month for a checkup and told him about my project.  I mentioned that I wanted as much data as I could get about myself, so he recommended the ‘super-duper’ blood workup instead of the normal one.   Here it is (click for readable version).

My Dec 2011 Bloodwork

So it turns out I have some issues.   I’m in the red zone for Triglicerides, VLDL Cholesterol, and especially for LDL-P and small LDL-P, where my values seem to be, quite significantly, off the chart.   The standard HDL and LDL values, as well as the ratios, seemed to be in the normal range, but I failed the test for these newfangled things.  The dash-P in LDL-P stands for particle, and it measures the size and number of particulates in your LDLs.

Wow.  So I did a little research on LDL-P.  It has been well known that elevated levels of LDL, combined with low HDLs, are a risk for heart disease.  But lots of people get heart attacks with normal LDL levels.  In the last ten years, the research is showing that not all LDLs are created equal – LDLs come in different shapes and sizes.  It turns out that – counterintuively – small particle LDLs are much more harmful than large particle LDLs.  The small ones are the ones that get stuck in arterial walls, while the large ones are more soluble and float on by harmlessly.   For two people with the same LDL levels, those with smaller particles are muchmore at risk for heart disease.

Oh boy.  I’ve got a family history of heart disease, so I think I need to take this seriously.    I’m not really interested in starting myself on medication (statins) that will last the rest of my life.  So, I’m going to really focus on  my new regime of increased exercise and healthy eating to make a dent in these numbers.  Also, I apparently  need to bulk up on fiber and omega-3s (oat encrusted salmon for dinner tonight I guess!).  I suppose I’ll give it 6 months of diet and exercise changes and then go  for more bloodwork.

But in general, I’m a little concerned about over-reaction to these kind of tests.  For one, I am not an expert.  For two, these kind of medical tests and advice seem to go in fads, and change every few years (eggs are bad!  eggs are good!).  So, for those of you out there who know anything about cardiac health and medicine – please feel free to let me know your view of my test scores and seriousness of this.  I’m happy to explore crowdsourcing my health care!

CTV Release 1.0

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When I started this project, I pointed out that in order to keep myself honest I was going to open source my data.   I’m not sure if anyone will ever look at this, but in theory making it public gives me that little extra peer pressure to keep this project going.

Well the time has come for my first release!

I have created a public Dropbox (accessible here, and  link on the sidebar) repository for anyone to access my data.  It is broken into monthly folders, and has a README file for a little more detail.   In brief, the monthly folders contain the following files:

Fitbit:  Steps, daily floors climbed, distance travelled  and calories expended – according to the Fitbit.  Also has my daily weight measurements and fat % from the Withings scale.  And BP measurements, when I do them.

Livestrong:  Contains details of my food logging, with a summary file and a (very) detailed file with the food log and the nutritional breakdown.

Fitlinxx:  This is the system at my gym tracking my fitness there (weight training).  It lists machines used and weight lifted (um, not very much weight, I’m afraid)

Runkeeper:  Logs of non-gym related exercise. Mostly running, but also skiing or anything else I do.

Rescuetime: ‘Productivity software’ that tracks the programs I am using on my computer.  A way to keep track of lollygagging.  Procrastination, thy name is Facebook.

All of my data is up for the month of January, and is complete.  There is some data for previous months, but it is a bit more spotty.

Some facts from my first month:

  • Weight loss from 1/1 to 1/31: 4.9 lbs
  • Days I remembered to set the Fitbit to track my sleep: 23
  • Avg sleep time: 6hr 26 minutes
  • Avg time to fall asleep: 8 minutes
  • Number of days I walked more than10K steps:  6
  • Avg floors climbed per day: 21.6
  • Times went to gym: 4  (sounds low, but it is about 3 times more than I usually go in a month)
  • Days I did food logging: 17
  • Days my food logging can be considered ‘complete’: 7

Rescuetime plot of my Facebook usage for January

My Month of Data


We are at the end of the first month of My Year of Data…and I have learned a lot.  In the next few days I’ll post about some reflections on the first month, what works and what doesn’t and where I think this is going.   But first, a plot (click for embiggened version):

Withings scale plot

This is a plot of my daily weight from the start of my project until now.   Thanks to my Withings scale, weight is one of the few things that I have been able to consistently measure, every single day, without forgetting.   Overall, I’m quite excited to have lost about 5 pounds, but I am a bit concerned about the wild swings back and forth.   That’s what the holidays do to you, I guess!  My detox in mid-January seems to have regained my downward momentum, and I’m hoping here at the end of January I may be establishing a new baseline.

Withings Plot: iPad version

(an aside here about the Withings tool:  here is the same plot as rendered using the iPad app – again, click for larger version.  Note that this picture has some kind of ‘confidence bands’ which are actually a nice way of visualizing the overall trends and give a sense of the overall variance of the process, or for those geekoids out there, a quick view of the signal to noise ratio.)

Both of these plots show a trendline, which I think is just a simple moving average.  You’ll notice that the trendline lags behind the weight swings – which is exactly what you want it to do.  Fast weight loss (and gain, to a lesser extent) is not ‘real’, it is mostly temporary due to water weight or simply a stuffed belly.   It takes about a week at an established weight level for the trend line to ‘catch up’ — at that point you can more confidently say that you have stabilized at that weight.

One purpose of this project is to see how weight is correlates with other things that I track.  Obviously the most important link to weight is food intake.  I’ve struggled with being consistent with my food logging- it requires a dedication and diligence I cannot seem to keep up.   I was hoping to see how the foods that I eat related to other aspects of my life, and that is hard to do without consistent data.  But I did learn something very interesting thanks to my missing data!  It is not necessarily *which foods* I log, but rather *whether I log my food at all* which is a good indicator of whether I am losing weight or gaining weight.

The plot below shows the same data as the Withings chart above (I imported the data into R for more flexibility).  Here the curve is a standard smoothing curve, and so it does not have the lag.  The background pink color is shown on the days where I did regular food logging – the blank days were my lapses in logging.   If you look at this closely, you will see that when I am not logging my food, this is when the curve is going up (generally).  I think this is mostly psychological — if I know I am ‘off-the-wagon’ and I am eating and snacking more than I should, I tend not to log my food intake.  Conversely, if I am really dedicated to food logging on a given day, I am much more likely to eat healthy.

To me, this is at least a small piece of evidence in favor of my hypothesis…that the act of measuring something will affect what is being measured.    And although I am kind of disappointed in my inability to stay on the food logging plan consistently, it’s already helped me to learn something interesting about myself.

Weight chart with food logging days in color



For the last few years, every January, my wife and I do some kind of detoxification.   It seems like a good time, after all of the excesses of the holiday season, to try and give the body a chance to re-generate and kick it back into shape.  This year, I get to track the results!

There are many different types of detox diets, but they all have the same goal in mind – to cleanse the system of the unnecessary toxins and bad guys that bombard our systems on a regular basis.  Most have the goal of ‘clearing out’ the liver and the colon by eating exclusively healthy foods for a short period of time.   Flush out the system so to speak.

Detox diets come in all shapes and sizes.  Most of them involve some kind of fasting, and some prohibit any solid food.   The most famous is the Master Cleanse – which only allows a tonic of lemons, maple syrup and cayenne syrup for a whole week!   That’s crizazlebeans, if you ask me.

The one we do is based on a book called the Fast Track Detox Diet.  What I like about it is that it is not really a diet at all, you can eat as much as you want, but you just have to follow the rules. In fact, there are some foods you are required to eat every day.   The rules are all made to load up on foods that support colon/liver health, and remove those foods that tend to ‘gum up the works’.  Here they are:

Must have at least one of each of the following groups every day:

  • Crucifers (broccoli, cauliflower, cabbage)
  • Green Leafys (Spinach, kale, parsely, cilantro, …)
  • Citrus (but not grapefruit)
  • Sulfurs (eggs, garlic, onion)
  • Liver-healers (Artichoke, Asparagus, Beet, Celery)
  • Colon-caring (flaxseed, carrot, apple, pear, berries)
  • 1-2 Tbls of Olive Oil
  • Lots and lots of water
  • 2 servings of protein (lean meat, fish or beans)

Must avoid the following

  • fried food
  • added sugar or sweets
  • gluten (at least avoid white flour and rice)
  • alcohol
  • dairy
  • caffeine

It’s actually a fun challenge to try and get all of the ‘good’ foods in daily, although a trip to a good salad bar can knock off most of this list for the day.   Avoiding gluten and dairy is a real challenge.    And caffeine….

We do a modified version of this.  We do avoid dairy milk and cheese, but do allow yogurt.   I  indulge my sweet tooth with a small piece of dark chocolate after dinner.  And the caffeine, well lets just say I try to cut back 🙂

The plan is to follow this diet for 7 days to clean out the system.   Then do a one day liquid fast (more on this in my next post).  Then three more days of the food plan while easing back into the real world.    Tracking it all the way.

We definitely find that we feel great after doing the detox, and the fasting day is not a big deal.  The goal of the detox is not weight loss, but undoubtedly I’ll drop 3-5 pounds during the process.   Anyone else out there have any detox experience?


Sleeping like a child


The Fitbit software makes an interesting attempt to measure ‘sleep effectiveness’.  When you go to sleep, you put the device into sleep mode by pushing and holding the button.  At that point it starts measuring your ‘micro-movements’ to determine how restful your sleep is.  The idea is that the less movement you have during the night, the better your sleep is.   There is actually some science behind this (called actigraphy), and it must be legitimate because it has a Wikipedia page.

Anyway, the Fitbit web site reports your sleep as a chart.  Here is a typical one for me:

The baseline blue bar is hardcore sleep-time, no movements.  The vertical red lines occur when a movement is detected.  Getting out of bed or a very sharp movement would result in a larger line.

Looks like I have a nice, long period of absolute inaction, with only a few movements during the night.  Saying I had 8 ‘Times Awakened’ is a bit of a stretch – I usually sleep pretty well and pretty hard.

They try and sum all of this up in to a 96% Sleep Efficiency.  I have no idea what that means, but it sounds good.

Just for kicks, I put the Fitbit on each of my kids for one night.  Here is my 5 year old’s chart:

I know that she is a wiggly sleeper, so I am not surprised that she had 22 times awakened.  It takes her longer to fall asleep than me, and she doesnt have any of those long periods of dead-cold slumber.

Here is the 9 year old:

This particular night she woke up in the middle of the night due to a nightmare about a bug.  The bigger line around 1:30am is her coming into our room  and climbing into our bed.  Looks like she got back to sleep pretty quickly!   Despite this, she has some good periods of hard sleep, and her sleep efficiency was better than mine!

Dont know really how useful this is, but it will be interesting to track trends as the year goes on.  I dont really have sleep problems, but if I did, I think this might be a useful way to track how effective different treatments might be.

Actually, most useful for me might be the ability to track my bedtime and overall sleep duration, which fluctuates with how busy I am at work and other factors which might change during the year.

Anyway, for those who do have sleep issues, there lots of new tracking devices, including specialized watches and brain wave reading head bands to wear while you sleep.   I think learning about my sleep cycles and REM sleep would be fascinating.   For those that dont want to wear a device, there are even iPhone apps that you put under your pillow  to track your movements that seem to work surprisingly well.

Leave a note below if you have any more experience with these trackers.  thanks!

What “Off The Wagon” Looks Like


I had fully intended to start my Year of Data on my 41st birthday in Mid-December.  But, as it turned out, the perfect storm of birthday celebrations, holiday celebrations, vacation, and an unfortunate destruction of my Fitbit sent me way off the wagon.

I learned a lot though.  The power of food logging is apparent in the following chart of my weight in the past month:

[Sorry for no date scale on the plot…it starts in mid-November, about the time I started this blog.  In a month, I lost about 5 lbs.  The low point is in mid-December, just before my birthday, when my data logging went off the rails. The last point is this morning].    You can see the slow gain of weight since mid-Dec, with Christmas cookies and treats readily available, and without the penalty of feeling like I have to log everything I eat.

Anyway, here we are in the New Year, and as of this morning, I am re-dedicated to the plan.   I’ve got a new Fitbit, I’ve got my data plan sorted out, and will be posting my data for the first time in the coming I’ve weeks.  got a bunch of ideas for new blog posts as well so the site won’t be quite as dead as it has been in the last few weeks.

Most importantly, a happy and healthy new year of data to all of you out there!

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