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):
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.
(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.