Comment on Time to Say Goodbye to the B.M.I.?

• Tar_alcaran@sh.itjust.works ⁨1⁩ ⁨month⁩ ago

  BMI has some great benefits, in that it’s really easy to get and you can’t mess it up. That’s why it’s a great measure for population and an amazing indicator for individuals.

  BMI = Heigh / (weight * weight)

  anyone who made it halfway through highschool can do this

  BRI = 364.2 - (365.5(sqrt(1-(waist circumference/(pi*height)^2)))

  I had to tripple check if I got the correct number of brackets, that’s how easy it is. And note that this isn’t even the useful number that’s used in the supporting paper. They validate their model by adding in other factors for waist and hip circumferences, age, height, gender, ethnicity, and body weight, to get bodyfat percentage and visceral adipose tissue.

  BMI is a spherical cow in vacuum, simple, easy, and close enough in basically all cases. And when it’s off, it’s usually off in being too conservative. BRI is a great tool for healthcare profesionals, but it requires too many measurements and too much math for the average person.

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  • mosiacmango@lemm.ee ⁨1⁩ ⁨month⁩ ago

    BMI is specifically not an amazing metric, for populations or individual people. The article goes into its flaws at length, but to summarize :

    Its based entirely on white men, with no demographic data for women or POC. It also fails utterly to account for muscle, so it classifies many very fit people as obese.

    Its only use is that it’s been in use for so long it can be used as a historical measurement to compare generations to each other.

    Complicated math in the age of smartphones is a non issue. It’s also not required if you do the simple thing I discussed above.

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    • databender@lemmy.world ⁨1⁩ ⁨month⁩ ago

      If a person is obese by BMI and the weight is actually muscle it’s obvious to everyone. They’re either in fantastic shape or they’re a powerlifter (all of who know the health risks of the amount of fat they’re carrying). BMI isn’t wildy off in ways that will surprise a doctor.

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    • Tar_alcaran@sh.itjust.works ⁨1⁩ ⁨month⁩ ago

      BMI is specifically not an amazing metric, for populations

      It’s a great measure for populations specifically because it doesn’t rely on getting extra measurements. Height is easy, weight is easy. These are things we have data on for huge groups of people without doing any extra work at all. If you want BRI, you need to go out and measure people, which is expensive, or you need to rely on self-reporting, which is almost always wrong.

      or individual people.

      That’s why I said it’s a good indicator, which is not the same thing.

      There are plenty of people who are .5 points short of being obese, but thanks to being very sedentary are still carrying too much fat, so it’s a bad metric. But if you’re .5 points short of being obese, you should take note of the fact that you’re basically obese, so it’s a good indicator.

      Its 200 years old, based nearly entirely on white men, with no design consideration for women or POC. It also fails utterly to account for muscle, so it classifies many very fit people as obese.

      This is such a stupid argument. A model that uses 2 variables does indeed not use every other variable in the universe. BRI doesn’t account for lymphedema, race, amputation, pregnancy, gender, hydrocephalus, weight, congestion, muscle, cardiac health, age and a million other variables.

      BRI uses exactly 2 measurements: Waist circumference and height, which is the exact same number of variables as BMI (height and weight). Which basically removes every argument except “BMI is older”, which is a very strange argument to make.

      Now, anticipating your reply: Using BRI to compute bodyfat and VAT requires many other inputs, like gender, race, age, etc, making it a more nuanced, better model. But that’s not what’s being discussed here. If you look up a “BMI based” bodyfat calculator, it will ask for your waist circumference. If you look up a “BRI based” one, it will ask for your weight. Those are completely different models doing something completely different.

      And this is exactly my problem with this paper. They say “This model is much better!” and then they proceed to not actually use their model in validation, but a derived model.

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