I recently made this data visualization that lets you compare your body measurements to the rest of the population and see where you fall on the bell curve. It’s unique in that it uses data to provide a really beautiful and ego-driven (and therefore motivational) approach to body measurement tracking. For example, if you have 10% bodyfat or 14 inch biceps or a 32 inch waist, it just feels a lot more meaningful to be able to say “my bodyfat is lower than 80% of the population” or “my arms are bigger than 70% of the population” or “my waist size is smaller than 75% of people” than to just look at those numbers without context.
Just wanted to share it on this forum because it seems like something that’s right up the alley of the Quantified Self community and I thought you guys might like to try it out and see how you stack up. Here are my results so if you don’t know your own measurements you can check out mine to see what it looks like filled out.
That’s great. I just added my data. I agree with your observation on giving anthropometric reference data (especially the percentiles).
I noticed that you used US army data for body fat (which they seem to have calculated using a body measurement equation). Have you considered using this CDC body composition survey on US population? https://www.cdc.gov/nchs/data/series/sr_11/sr11_250.pdf
The reference data is more representative and can also compare you with subjects with similar age and ethnicity. I’m asking because I have used it, but it works mostly if the body fat results are from a DEXA machine (which is not common). I can’t seem to find an “equivalent” and/or a way to “convert” their classification into something that applies to other body fat measurement tools (e.g. bod pod, hydrostatic weighing, scales or body fat formulas). Any ideas?
Oh wow this is a great data set! I’ll have to look into incorporating it in the future. The data I used for civilian bodyfat numbers was actually from the CDC’s National Health and Nutrition Examination Survey, which used bioelectircal impedence. I know that method is pretty inaccurate but at the time it was the best data set I could find for civilians. I also figured on a population-wide basis the error factor on bioelectrical impedence would wash out a bit. Clearly this DEXA data set is better though.
As far as converting between different body fat measurement techniques I’m not aware of any good method. I think this would be difficult to do because if any of the methods were known to consistently over or under estimate bodyfat in a way that could be adjusted like that, the formulas themselves could just be adjusted to be more accurate to reality rather than converting between methods.
So the tool basically just assumes the accuracy of the number it estimates based on body measurements and also assumes the accuracy of the population-wide data sets and directly compares them. It’s imperfect, but short of using the data set you’ve provided and asking people for DEXA results (which most won’t have) I can’t really think of a better way.