Apologies for the absence. A brand new research project is getting cranked up and I’ve been pretty short on time. As such the posting schedule may get a bit erratic. I’ll do my best.
I’m also working on improving some other things as well. When I started college I somehow managed to avoid the freshman 15 (the weight lots of people gain during college), but nevertheless ended my four years about that much heavier. That wasn’t a bad thing. Lots of it was muscle from doing a bit of working out, and I was pretty slight going into college in the first place.
The problem is that going into grad school I did it again, minus the working out. That’s not a trend I want to maintain, and so I’ve embarked on a project to reverse the process. Here’s the results thus far. Y-axis in pounds, x-axis in days, day 1 is 8/12/09, data taken first thing each morning for consistency:
There’s no special method or system I’m following, just the old calories in < calories out. To do that I’ve just traded in Coke for water, reduced the number of fast food meals (and I don’t get fries when I do), followed a “no junk” rule when shopping for food, and stepped up the walking / stair climbing / pushups / situps / etc. Eventually I’m going to add running to that. I’m not looking forward to it, but it needs to be done.
If you Fourier transformed this data set you’d probably see a spike near the 1 per week mark, as healthy eating tends to bite the dust when doing weekend grilling with friends. But that’s ok. The point is to get the average calorie burn rate above the average intake, and a weekend reversal of reasonable size will only dent the average a bit. The average is what I track using the linear best-fit, shown by the line and its associated equation. The linear fit is one of the all-time most useful tools in the experimentalist’s kit, allowing first-order relationships to be teased out of noisy data. The way it works is to draw a line and calculate the distance between each point and the line. Square those distances and add them up. The best-fit line is the one for which that sum is smallest. We don’t have to do this by trial and error, there’s a simple procedure due to Gauss (and Legendre, long story) which does the whole thing in one step. The R^2 value is an indication of how well the data points are fitted by the line. The value here of 0.76 indicates a relatively good fit.
In this case the slope of the line (which represents the pounds-per-day change) is -0.108, corresponding to a loss of 0.108 pounds per day, or about 3/4 pound per week. Nothing dramatic at all, but then I’m not looking for dramatic. (For reference, by the way, I am 5’9″) One pound of fat corresponds to around 3500 food calories, so my daily calorie deficit averages 378. This is a bit of an indication of how fragile the weight loss process can be, since one extra slice of pizza can nuke a day’s progress. On the other hand, it’s also a sign of how easy it can be to get on the right track – cut out just a little and you’re making good headway. I think charting it has helped me stick with things. Motivation is tough when visible indications of success are so slow, but the chart helps you see the progress in the daily variability.
How far to take it? My original goal was “below 160″. With current progress I’ll be there soon – statistically, on the 26th of this month. Fingers crossed.