1 00:00:00,320 --> 00:00:03,000 Speaker 1: Brought to you by the reinvented two thousand twelve Camray. 2 00:00:03,240 --> 00:00:10,000 Speaker 1: It's ready. Are you welcome to stop Mom Never told You? 3 00:00:10,200 --> 00:00:18,320 Speaker 1: From housetop works dot com. Hello, and welcome to the podcast. 4 00:00:18,400 --> 00:00:22,920 Speaker 1: I'm Kristen, I'm Molly, so Molly. It's after the holidays. 5 00:00:23,000 --> 00:00:27,560 Speaker 1: We have gotten survived the holidays season, you do know, 6 00:00:27,600 --> 00:00:29,920 Speaker 1: Martin Luther King Day's coming up. I know, thank goodness, 7 00:00:30,000 --> 00:00:33,360 Speaker 1: another another holiday to look forward to. But it's around 8 00:00:33,400 --> 00:00:36,640 Speaker 1: the holidays that around this time, especially after the holidays, 9 00:00:36,640 --> 00:00:39,080 Speaker 1: with New Year's resolutions and everything, we start kind of 10 00:00:39,520 --> 00:00:44,800 Speaker 1: assessing our weight, our physical fitness, seeing what trouble spots 11 00:00:44,880 --> 00:00:47,600 Speaker 1: we might need to work on. Which brings to mind 12 00:00:47,640 --> 00:00:50,720 Speaker 1: a little measure called body mass index. Yeah, and it's 13 00:00:50,720 --> 00:00:52,920 Speaker 1: pretty easy to find out your own body mass index. 14 00:00:53,000 --> 00:00:55,720 Speaker 1: You go online, you google this, You'll be will enter 15 00:00:55,920 --> 00:00:58,240 Speaker 1: your weight and your height and find out a number 16 00:00:58,720 --> 00:01:02,560 Speaker 1: that's between eight and thirty five ish is. It's sort 17 00:01:02,560 --> 00:01:05,040 Speaker 1: of like that where the calculations kind of run and 18 00:01:05,080 --> 00:01:09,360 Speaker 1: if it falls within a certain range, then you're considered normal, overweight, 19 00:01:09,760 --> 00:01:13,520 Speaker 1: or obese. Yeah, and this seems like a pretty pretty 20 00:01:13,560 --> 00:01:17,920 Speaker 1: quick simple assessment of your weight, but there was some 21 00:01:18,080 --> 00:01:23,600 Speaker 1: controversy earlier this year about how one university was using 22 00:01:23,640 --> 00:01:27,520 Speaker 1: bm I to determine whether or not students could graduate. Right. 23 00:01:27,800 --> 00:01:30,480 Speaker 1: This was at Lincoln University in Pennsylvania. It was the 24 00:01:30,520 --> 00:01:32,759 Speaker 1: in part of two thousand nine who probably heard about 25 00:01:32,760 --> 00:01:35,880 Speaker 1: this story. Uh, The school was requiring students with the 26 00:01:35,880 --> 00:01:39,000 Speaker 1: body mass index of thirty or above to take a 27 00:01:39,080 --> 00:01:42,600 Speaker 1: fitness course in order to graduate, and this raised a 28 00:01:42,600 --> 00:01:45,240 Speaker 1: lot of controversy about whether, you know, it's a school's 29 00:01:45,280 --> 00:01:48,160 Speaker 1: responsibility to tell a person that they might have a 30 00:01:48,200 --> 00:01:51,320 Speaker 1: health problem. Um. You know, students were saying that this 31 00:01:51,320 --> 00:01:54,240 Speaker 1: shouldn't be a requirement to graduate, that their academic records 32 00:01:54,240 --> 00:01:57,279 Speaker 1: should be UM. But one of the factors that always 33 00:01:57,280 --> 00:01:59,200 Speaker 1: comes up, I think when b m I is involved 34 00:01:59,280 --> 00:02:01,920 Speaker 1: is that it's not the most accurate thing. Now to 35 00:02:01,960 --> 00:02:03,560 Speaker 1: make sure that they are really getting students who they 36 00:02:03,600 --> 00:02:07,920 Speaker 1: considered obese, the school went on to measure the students waste, 37 00:02:08,360 --> 00:02:11,320 Speaker 1: hoping that that would weed out people like athletes who 38 00:02:11,360 --> 00:02:15,639 Speaker 1: have um higher weights but are still healthy for their heights. Right, 39 00:02:15,680 --> 00:02:18,040 Speaker 1: because people with really high b m I s and 40 00:02:18,080 --> 00:02:20,320 Speaker 1: the overweight and obese category might be at a higher 41 00:02:20,360 --> 00:02:23,760 Speaker 1: risk for things like heart disease and type two diabetes. 42 00:02:23,800 --> 00:02:25,960 Speaker 1: So the college was arguing that, hey, we're trying to, 43 00:02:26,480 --> 00:02:29,239 Speaker 1: you know, do this for our students better health in 44 00:02:29,320 --> 00:02:34,560 Speaker 1: the future. UM. But we recently found out that because 45 00:02:34,560 --> 00:02:38,320 Speaker 1: there is so much hubbub surrounding this issue, to school 46 00:02:38,440 --> 00:02:41,760 Speaker 1: just basically dropped it. But the issue of whether or 47 00:02:41,800 --> 00:02:43,560 Speaker 1: not b m I is actually a good predictor of 48 00:02:43,639 --> 00:02:48,160 Speaker 1: health is still pretty controversial. So let's talk about it. 49 00:02:48,200 --> 00:02:49,480 Speaker 1: Do you want to talk about all the ways we 50 00:02:49,560 --> 00:02:52,160 Speaker 1: can measure obesity, Kristen, Yeah, let's talk about like what 51 00:02:52,240 --> 00:02:55,399 Speaker 1: exactly the b m I formula is and go from there. 52 00:02:55,480 --> 00:02:57,600 Speaker 1: So the b m I, your b m I is 53 00:02:58,160 --> 00:03:05,200 Speaker 1: your weight divided by your height squared times seven and three. Now, wait, 54 00:03:05,240 --> 00:03:08,480 Speaker 1: that all depends on whether you're doing metric system right 55 00:03:08,760 --> 00:03:11,360 Speaker 1: or just standard. Yeah, this is this is just I 56 00:03:11,400 --> 00:03:14,080 Speaker 1: hate metric. Yeah, this is this is you know for 57 00:03:14,160 --> 00:03:17,520 Speaker 1: people in the in the US of A UM hate metric, 58 00:03:18,200 --> 00:03:23,200 Speaker 1: like Molly anti metric, Molly new nickname. Um, that's the 59 00:03:23,320 --> 00:03:25,519 Speaker 1: that's the formula. And there there's another formula for the 60 00:03:25,560 --> 00:03:28,240 Speaker 1: metric system, which I think it's actually easier. I don't 61 00:03:28,240 --> 00:03:29,639 Speaker 1: think you have to do anything with seven. Oh, three 62 00:03:29,760 --> 00:03:32,919 Speaker 1: probably is metric is far easier and makes a lot 63 00:03:32,919 --> 00:03:36,520 Speaker 1: more sense. However, that is the so that's to be 64 00:03:36,560 --> 00:03:39,120 Speaker 1: in my formula. But like you said, there are a 65 00:03:39,200 --> 00:03:43,160 Speaker 1: few other, even more reliable ways to assess your your 66 00:03:43,160 --> 00:03:45,880 Speaker 1: body fat content. Now we just mentioned that Lincoln would 67 00:03:45,920 --> 00:03:48,920 Speaker 1: measure a student's waste and this is known as the 68 00:03:48,960 --> 00:03:51,080 Speaker 1: waiste to hip ratio, which is getting a lot of 69 00:03:51,120 --> 00:03:54,080 Speaker 1: attention lately as a hip ha ha ha new way 70 00:03:54,160 --> 00:03:57,360 Speaker 1: to measure ob cit. What you do here is you 71 00:03:57,400 --> 00:04:01,680 Speaker 1: take a tape measure and you you measure your waist 72 00:04:01,840 --> 00:04:05,680 Speaker 1: at its smallest point and your hips at their largest point. 73 00:04:06,360 --> 00:04:08,720 Speaker 1: Divide them and you should get a ratio. And they 74 00:04:08,760 --> 00:04:11,360 Speaker 1: don't know yet what the perfectly healthy ratio is, but 75 00:04:11,400 --> 00:04:13,880 Speaker 1: they're saying that if you're a female, if the ratio 76 00:04:14,000 --> 00:04:18,520 Speaker 1: is less than one, then generally you're you're okay. So 77 00:04:18,560 --> 00:04:20,800 Speaker 1: we've got we've got b m I, we've got waste 78 00:04:20,839 --> 00:04:24,680 Speaker 1: of hip ratio. We also have the old pinch test. Okay. 79 00:04:24,760 --> 00:04:28,520 Speaker 1: That's basically where you take the some calipers to measure 80 00:04:28,640 --> 00:04:31,679 Speaker 1: the folds of skin in certain spots in your body. 81 00:04:31,760 --> 00:04:34,000 Speaker 1: This is not a fun test to take. Molly and 82 00:04:34,000 --> 00:04:37,800 Speaker 1: I can speak from experience, I had to take a 83 00:04:37,839 --> 00:04:41,360 Speaker 1: pinch test, uh in I believe it was eleventh grade 84 00:04:41,440 --> 00:04:46,200 Speaker 1: in health class in high school, and uh traumatized you 85 00:04:46,240 --> 00:04:51,440 Speaker 1: sound It wasn't traumatizing to me. The idea of you know, 86 00:04:51,560 --> 00:04:54,279 Speaker 1: going to gym class and having your fat pinch was 87 00:04:54,320 --> 00:04:56,760 Speaker 1: not a good idea of to you know, for sixteen 88 00:04:56,760 --> 00:05:00,920 Speaker 1: and seventeen year old girls. But that it's another way 89 00:05:01,080 --> 00:05:02,839 Speaker 1: to do it. But it's not very accurate. That's the 90 00:05:02,880 --> 00:05:05,240 Speaker 1: thing is, if it's just some gym teacher who's borrowing 91 00:05:05,279 --> 00:05:07,880 Speaker 1: the calipers from the health department, you can get a 92 00:05:08,320 --> 00:05:12,280 Speaker 1: vastly incorrect reading. Uh we're reading in US Musical Report 93 00:05:12,279 --> 00:05:15,720 Speaker 1: that let's say you've got body fat um or that's 94 00:05:15,720 --> 00:05:17,240 Speaker 1: what the caliper shaw that you have. That could be 95 00:05:17,240 --> 00:05:19,520 Speaker 1: anywhere from two to twenty eight. You know, just a 96 00:05:19,560 --> 00:05:21,680 Speaker 1: big margin of error if you're not using the pinchers 97 00:05:21,760 --> 00:05:25,440 Speaker 1: exactly right. So something that's a little bit more accurate 98 00:05:25,480 --> 00:05:27,800 Speaker 1: according to US mus Normal Report is something called the 99 00:05:27,800 --> 00:05:30,679 Speaker 1: bioelectrical test. And this is where a technician will attach 100 00:05:30,800 --> 00:05:34,360 Speaker 1: electrodes to one hand and one foot and they run 101 00:05:34,520 --> 00:05:39,320 Speaker 1: a harmless specify harmless radio frequency pulse through your body 102 00:05:39,320 --> 00:05:43,960 Speaker 1: to measure its water content. I mean space age space age, 103 00:05:43,960 --> 00:05:46,240 Speaker 1: but again it's not doesn't I guess no way of 104 00:05:46,240 --> 00:05:48,000 Speaker 1: measuring body fat is going to be tons of fun. 105 00:05:48,240 --> 00:05:51,839 Speaker 1: But I can't imagine having radio frequency running through me 106 00:05:52,520 --> 00:05:54,719 Speaker 1: would be good. Um, but this isn't a lot of 107 00:05:54,720 --> 00:05:58,839 Speaker 1: health clubs, medicine clinics. It's not very it's not very available. 108 00:05:58,880 --> 00:06:02,280 Speaker 1: Let's say to the general pulp like, also not very available. 109 00:06:02,279 --> 00:06:05,200 Speaker 1: It's something called the bod pod. This is my favorite 110 00:06:05,200 --> 00:06:09,360 Speaker 1: one I would love. It's an egg shaped chamber and 111 00:06:09,400 --> 00:06:12,200 Speaker 1: you climb in, you sit there for twenty seconds, and 112 00:06:12,240 --> 00:06:14,720 Speaker 1: it can measure air displacement and tell you exactly how 113 00:06:14,800 --> 00:06:19,080 Speaker 1: much body fat you have. So bod pods pretty spot 114 00:06:19,120 --> 00:06:21,200 Speaker 1: on with this seems like so far it's sort of 115 00:06:21,240 --> 00:06:23,760 Speaker 1: the golden standard. And as most things that are the 116 00:06:23,760 --> 00:06:26,600 Speaker 1: golden standard are, it's very expensive and so it's only 117 00:06:26,680 --> 00:06:29,640 Speaker 1: a few hospitals. It's really not available to the general public, 118 00:06:29,680 --> 00:06:32,159 Speaker 1: which is why the general public is still left finding 119 00:06:32,160 --> 00:06:35,200 Speaker 1: a body mass index calculator online and using that to 120 00:06:35,200 --> 00:06:37,359 Speaker 1: figure out how much body fat they have. Now for 121 00:06:37,400 --> 00:06:40,839 Speaker 1: the last test, Smalley, the immersion test. This one seems 122 00:06:40,960 --> 00:06:43,400 Speaker 1: like the most pleasant one. To do, because you basically 123 00:06:43,400 --> 00:06:45,320 Speaker 1: just get in a pool of water and get dunked 124 00:06:45,320 --> 00:06:48,240 Speaker 1: a few times. Uh. First you will expel all the 125 00:06:48,320 --> 00:06:51,080 Speaker 1: air from your lungs and uh and they dunk you 126 00:06:51,120 --> 00:06:54,160 Speaker 1: in a pool half a dozen times. And it says 127 00:06:54,200 --> 00:06:56,920 Speaker 1: that under or overestimates your body fat by only one 128 00:06:56,960 --> 00:07:00,479 Speaker 1: percentage points. So still pretty accurate. Yeah, is, but you 129 00:07:00,480 --> 00:07:02,880 Speaker 1: know that's just the recipe for the worst day ever 130 00:07:03,000 --> 00:07:05,640 Speaker 1: to me jump to make a jump in a pool 131 00:07:05,680 --> 00:07:08,000 Speaker 1: for an hour and then here how much body fat 132 00:07:08,040 --> 00:07:11,120 Speaker 1: I have? Like, if that's not a recipe for overreading 133 00:07:11,160 --> 00:07:12,600 Speaker 1: ice cream, I don't know what it is. But if 134 00:07:12,600 --> 00:07:14,040 Speaker 1: you've had any of those tests, I want to hear 135 00:07:14,040 --> 00:07:15,600 Speaker 1: about them, if you've been in a body pod, I 136 00:07:15,640 --> 00:07:18,040 Speaker 1: want to know how cool it is. But here's my 137 00:07:18,120 --> 00:07:21,000 Speaker 1: question to Molly. We have all of these other ways 138 00:07:21,080 --> 00:07:24,960 Speaker 1: to assess body fat that are much that are very accurate, 139 00:07:24,960 --> 00:07:27,920 Speaker 1: that are far more accurate. But Molly, my question is, 140 00:07:28,080 --> 00:07:31,160 Speaker 1: we have all of these different options for measuring body 141 00:07:31,160 --> 00:07:35,560 Speaker 1: fat and a lot of which have far more accurate results. However, 142 00:07:35,760 --> 00:07:38,680 Speaker 1: b m I is still our go to measure, right, 143 00:07:38,880 --> 00:07:41,000 Speaker 1: That's what they're using at Lincoln. When you ever you 144 00:07:41,040 --> 00:07:44,480 Speaker 1: read articles about health risk, heart disease, things like that. 145 00:07:44,520 --> 00:07:46,960 Speaker 1: People are always going to say, look your b m I, 146 00:07:48,160 --> 00:07:51,160 Speaker 1: But why why are we looking at bm I? Well, Molly, 147 00:07:51,200 --> 00:07:53,760 Speaker 1: should we go back in time? Perhaps it's my favorite 148 00:07:53,760 --> 00:07:56,400 Speaker 1: thing to do on this podcast, tell the story of 149 00:07:56,440 --> 00:07:59,520 Speaker 1: the body mass index, which is actually a little more 150 00:07:59,600 --> 00:08:02,480 Speaker 1: it's more interesting than than this probably sounds at this 151 00:08:02,520 --> 00:08:06,679 Speaker 1: point listeners. So because there's someone involved named Adolph wheat 152 00:08:06,760 --> 00:08:10,160 Speaker 1: Lit Wheatlet, and he's from Belgium, and all good stories 153 00:08:10,160 --> 00:08:12,760 Speaker 1: start in Belgium. So he's the one who came up 154 00:08:12,800 --> 00:08:15,960 Speaker 1: with this equation eighteen thirty two, not because he had 155 00:08:16,000 --> 00:08:19,480 Speaker 1: this overwhelming desire to study obesity, but because he was 156 00:08:19,520 --> 00:08:22,240 Speaker 1: doing this study on normal man. That means he was 157 00:08:22,280 --> 00:08:25,120 Speaker 1: looking at everything about man that was average to figure 158 00:08:25,160 --> 00:08:27,840 Speaker 1: out just what the most perfectly average man would look like, 159 00:08:27,880 --> 00:08:30,840 Speaker 1: from his arm length to his level length, to his obesity. 160 00:08:30,920 --> 00:08:32,320 Speaker 1: And I think that we should know that we got 161 00:08:32,360 --> 00:08:36,000 Speaker 1: all of this great information from an article on slate. 162 00:08:36,640 --> 00:08:40,760 Speaker 1: So Queetlet went around collecting all of his data from 163 00:08:41,160 --> 00:08:45,360 Speaker 1: several hundred countrymen, and he found that there were correlations 164 00:08:45,400 --> 00:08:48,480 Speaker 1: between a man's weight and height. Basically that your weight 165 00:08:48,520 --> 00:08:51,160 Speaker 1: is going to be proportional to your heights. Say, you know, 166 00:08:51,280 --> 00:08:54,600 Speaker 1: if I'm uh ten taller than you, I'm probably gonna 167 00:08:54,600 --> 00:08:56,960 Speaker 1: wait ten percent more than you as well. So if 168 00:08:57,040 --> 00:08:59,040 Speaker 1: he makes this pretty decent finding, but it's not like 169 00:08:59,200 --> 00:09:02,360 Speaker 1: Dr Stitch bonded immediately to say, oh quite like you've 170 00:09:02,400 --> 00:09:05,040 Speaker 1: discovered how to measure this, it kind of sits dormant 171 00:09:05,080 --> 00:09:08,120 Speaker 1: for a while. Yeah, because but back then, doctors weren't 172 00:09:08,120 --> 00:09:14,400 Speaker 1: really linking illness to obesity. Um, the first large scale 173 00:09:14,400 --> 00:09:16,920 Speaker 1: studies of obesity and health didn't start until early in 174 00:09:16,960 --> 00:09:21,720 Speaker 1: the twentieth century with the rise of insurance companies. I 175 00:09:21,720 --> 00:09:24,040 Speaker 1: find this really interesting because in all this talk of 176 00:09:24,080 --> 00:09:26,760 Speaker 1: healthcare form, you know, I mean, you kind of wonder 177 00:09:26,840 --> 00:09:29,080 Speaker 1: if we could save a lot of healthcare costs by 178 00:09:29,200 --> 00:09:31,400 Speaker 1: coming out and telling people, hey, you're unhealthy, you need 179 00:09:31,440 --> 00:09:33,520 Speaker 1: to change your habits, which is what Lincoln was trying 180 00:09:33,559 --> 00:09:36,480 Speaker 1: to do the school. So just better than in mind 181 00:09:36,480 --> 00:09:39,720 Speaker 1: when you think about what organization's role is and telling 182 00:09:39,720 --> 00:09:43,120 Speaker 1: people that they're unhealthy. But here's what the insurance companies do. 183 00:09:43,200 --> 00:09:46,680 Speaker 1: They want to show their policy holders that yes, you 184 00:09:46,720 --> 00:09:50,680 Speaker 1: were costing us more if you're overweight. Because you overweight 185 00:09:50,679 --> 00:09:53,040 Speaker 1: people are dying earlier than those of a so called 186 00:09:53,360 --> 00:09:55,880 Speaker 1: ideal weights. You're more likely to get diabetes, you more 187 00:09:55,920 --> 00:09:57,880 Speaker 1: likely get heart disease, and we're paying out the nose 188 00:09:57,960 --> 00:10:02,200 Speaker 1: for that. So what these insurance companies need basically is 189 00:10:02,240 --> 00:10:06,319 Speaker 1: a quick function to show how body fat is related 190 00:10:06,360 --> 00:10:08,960 Speaker 1: to your heightened weight. Yeah, and they still and back 191 00:10:09,000 --> 00:10:12,520 Speaker 1: then they had like the caliper skin fold testing UM 192 00:10:12,559 --> 00:10:16,400 Speaker 1: and hydrostatic weighing with the basically the immersion tests that 193 00:10:16,400 --> 00:10:19,240 Speaker 1: that we mentioned. But it wasn't until nineteen seventy two 194 00:10:19,280 --> 00:10:23,520 Speaker 1: that the physiology professor an obesity researcher named Ansel Keys 195 00:10:24,120 --> 00:10:29,880 Speaker 1: published the Landmark Indices of Relative Weight and Obesity which 196 00:10:29,960 --> 00:10:34,640 Speaker 1: he studied seventy men in five different countries to assess 197 00:10:34,720 --> 00:10:40,640 Speaker 1: different heightened weight formulas that correlated to the best measurement 198 00:10:40,760 --> 00:10:44,840 Speaker 1: of their body fat percentage. And with this study he 199 00:10:45,000 --> 00:10:49,960 Speaker 1: found that Quainlets formula was the best. It was the best, 200 00:10:50,000 --> 00:10:52,839 Speaker 1: but he noted that it was the best at population 201 00:10:52,920 --> 00:10:55,120 Speaker 1: studies is a good way for doctors to get an 202 00:10:55,160 --> 00:10:59,840 Speaker 1: idea of how a certain population was um overweight or 203 00:10:59,840 --> 00:11:02,880 Speaker 1: not overweight. It was never meant to be used on 204 00:11:02,920 --> 00:11:06,679 Speaker 1: an individual diagnosis scale, right and and Keys was the 205 00:11:06,720 --> 00:11:10,320 Speaker 1: one who renamed Quitla's formula the body mass index, and 206 00:11:10,360 --> 00:11:15,560 Speaker 1: then five the National Institutes i FELL started defining obesity 207 00:11:15,600 --> 00:11:18,559 Speaker 1: according to b m I. And what I thought was 208 00:11:18,600 --> 00:11:20,559 Speaker 1: really interesting in this late articles. When the n i 209 00:11:20,679 --> 00:11:23,800 Speaker 1: H first started using body mass index to define obesity, 210 00:11:23,840 --> 00:11:27,440 Speaker 1: it was very specific in terms of gender and that 211 00:11:27,520 --> 00:11:30,560 Speaker 1: you could have UM b m I measure for a 212 00:11:30,600 --> 00:11:33,240 Speaker 1: woman and a man, and the numbers were different. They 213 00:11:33,240 --> 00:11:36,480 Speaker 1: were kind of um odd numbers, like twenty seven point 214 00:11:36,640 --> 00:11:39,360 Speaker 1: eight was a cut off for ABC. Wharas now everything 215 00:11:39,400 --> 00:11:43,320 Speaker 1: sort of on the five. That's how they determine if 216 00:11:43,320 --> 00:11:46,960 Speaker 1: you're overweight or not. UM. But then they just wanted 217 00:11:46,960 --> 00:11:49,640 Speaker 1: to sort of sound like standardize everything, and that means 218 00:11:49,679 --> 00:11:51,880 Speaker 1: that both men and women's b m I was measured 219 00:11:51,920 --> 00:11:54,200 Speaker 1: the same way, despite the fact that women have a 220 00:11:54,200 --> 00:11:57,400 Speaker 1: lot of more body fat and UM. Like I said, 221 00:11:57,559 --> 00:12:00,360 Speaker 1: everything became on the five, so it was easy to member. 222 00:12:00,679 --> 00:12:02,000 Speaker 1: I mean, you know, you knew as soon as you 223 00:12:02,080 --> 00:12:03,920 Speaker 1: hit thirty on your b m I that that was 224 00:12:04,080 --> 00:12:06,760 Speaker 1: a bad thing. So not only do we have this 225 00:12:06,800 --> 00:12:11,079 Speaker 1: measure that was originally intended for population studies rather than 226 00:12:11,120 --> 00:12:15,040 Speaker 1: individual diagnoses, we also have the NIH coming in and 227 00:12:15,240 --> 00:12:19,559 Speaker 1: sort of further watering down those standards. So we were 228 00:12:19,559 --> 00:12:22,560 Speaker 1: starting to see how the b M I might be 229 00:12:22,880 --> 00:12:26,520 Speaker 1: might not be the best predictor of your health because, 230 00:12:26,520 --> 00:12:28,680 Speaker 1: like you said, just to throw out a couple of things, 231 00:12:28,840 --> 00:12:32,400 Speaker 1: like b M I is the same for gender race. 232 00:12:32,480 --> 00:12:35,080 Speaker 1: There are some differences for children and teens, but for 233 00:12:35,200 --> 00:12:37,920 Speaker 1: the adult population, a b M I is a b 234 00:12:38,040 --> 00:12:39,760 Speaker 1: m I s bm I. It doesn't differ for men 235 00:12:39,760 --> 00:12:42,360 Speaker 1: and women. However, like you said, women have tend to 236 00:12:42,400 --> 00:12:44,880 Speaker 1: have more body fat than men, and at the same 237 00:12:44,960 --> 00:12:47,280 Speaker 1: b m I, older people will tend to have more 238 00:12:47,320 --> 00:12:51,760 Speaker 1: body fat than younger adults, which makes sense the mass exactly. 239 00:12:51,960 --> 00:12:55,520 Speaker 1: And then speaking of muscle mass, highly trained athletes who 240 00:12:55,520 --> 00:12:59,640 Speaker 1: are very muscular will probably have a very high b 241 00:12:59,800 --> 00:13:02,600 Speaker 1: m I that might put them in an overweight category 242 00:13:02,760 --> 00:13:07,000 Speaker 1: because it's just looking at body mass rather than differentiating 243 00:13:07,000 --> 00:13:10,400 Speaker 1: between fat and muscle. Right, So, you can find many 244 00:13:10,480 --> 00:13:13,720 Speaker 1: articles on the internet that just uh to cry down 245 00:13:13,720 --> 00:13:15,959 Speaker 1: with the b m I. It's not very accurate. Please 246 00:13:16,000 --> 00:13:18,160 Speaker 1: don't go on the internet and decide if your overweight 247 00:13:18,240 --> 00:13:20,400 Speaker 1: just based on a b m I. UM. A really 248 00:13:20,400 --> 00:13:22,679 Speaker 1: good example of how this can work against you was 249 00:13:22,720 --> 00:13:25,559 Speaker 1: a New York Times article from two thousand seven that 250 00:13:25,640 --> 00:13:28,360 Speaker 1: Kristen bound. It's called how does your waistline matter? Let 251 00:13:28,440 --> 00:13:31,960 Speaker 1: us count the ways ha ha ha um. And this 252 00:13:32,040 --> 00:13:34,559 Speaker 1: woman had a perfectly normal b m I. She didn't 253 00:13:34,600 --> 00:13:37,400 Speaker 1: have many risk factors for heart disease. But then her 254 00:13:37,440 --> 00:13:40,440 Speaker 1: doctor whips out a tape measure and does the old 255 00:13:40,440 --> 00:13:42,160 Speaker 1: ways to hit rate she and finds out that she 256 00:13:42,360 --> 00:13:44,760 Speaker 1: is actually in a high risk category for developing some 257 00:13:44,840 --> 00:13:47,880 Speaker 1: of these long term health problems. Now, while that New 258 00:13:47,960 --> 00:13:51,840 Speaker 1: York Times article highlights a couple of individual stories, I 259 00:13:51,880 --> 00:13:54,520 Speaker 1: think we should also know that there have been a 260 00:13:54,520 --> 00:13:57,920 Speaker 1: few large scale studies that have called the b m 261 00:13:57,920 --> 00:14:00,839 Speaker 1: I into question. For instance, there's on from a recent 262 00:14:00,880 --> 00:14:05,079 Speaker 1: one from the National Institutes of Health that compared people's 263 00:14:05,120 --> 00:14:09,040 Speaker 1: b m I s against results from the biolectrical analysis 264 00:14:09,120 --> 00:14:13,720 Speaker 1: that we talked about earlier, and it found, um for instance, 265 00:14:14,040 --> 00:14:18,480 Speaker 1: a big gender gap in these measurements, where where with men, 266 00:14:18,679 --> 00:14:21,320 Speaker 1: the b m I had a better correlation with their 267 00:14:21,320 --> 00:14:25,280 Speaker 1: with their lean mass, while in women the BF or 268 00:14:25,320 --> 00:14:29,280 Speaker 1: the bioelectrical test was a little more accurate predictor. And 269 00:14:29,360 --> 00:14:32,720 Speaker 1: they basically concluded that the b m I is is 270 00:14:32,760 --> 00:14:37,160 Speaker 1: pretty limited in how well it assesses obesity, and in 271 00:14:37,200 --> 00:14:39,720 Speaker 1: this case it missed more than half the people with 272 00:14:39,880 --> 00:14:44,200 Speaker 1: excess fat. And in addition, the Slate article also mentioned 273 00:14:44,280 --> 00:14:47,520 Speaker 1: a critique that was published in the journal Circulation that 274 00:14:47,720 --> 00:14:50,680 Speaker 1: said that since b m I is used in so 275 00:14:50,720 --> 00:14:53,360 Speaker 1: many of these large scale just health studies that we 276 00:14:53,480 --> 00:14:56,560 Speaker 1: that we see, and since b m I might also 277 00:14:56,640 --> 00:15:00,200 Speaker 1: not be very accurate, we don't know how the that's 278 00:15:00,200 --> 00:15:02,680 Speaker 1: affecting all of this health research that's going on. So 279 00:15:02,720 --> 00:15:05,600 Speaker 1: this could have a ripple effect in two areas beyond 280 00:15:05,680 --> 00:15:08,240 Speaker 1: just obesity. So basically, you can't trust your b m 281 00:15:08,240 --> 00:15:10,160 Speaker 1: I if it, I mean, it may not be showing 282 00:15:10,200 --> 00:15:11,800 Speaker 1: that you are overweight, and may be showing that you 283 00:15:11,840 --> 00:15:14,600 Speaker 1: are overweight and you're actually not. But it doesn't seem 284 00:15:14,600 --> 00:15:16,760 Speaker 1: to be going anywhere just because, as we said, it's 285 00:15:16,800 --> 00:15:19,600 Speaker 1: so easy. Anyone can check their b m I. So 286 00:15:20,240 --> 00:15:22,840 Speaker 1: um right now the big guidelines, even though the n 287 00:15:22,880 --> 00:15:24,760 Speaker 1: i H just found it's not the most accurate thing. 288 00:15:25,240 --> 00:15:28,840 Speaker 1: It through its National Heart, Long and Blood Institute, has 289 00:15:28,880 --> 00:15:31,560 Speaker 1: sort of tried to bring the waste hip ratio more 290 00:15:31,800 --> 00:15:34,520 Speaker 1: into a three legged stool, if you will, the three 291 00:15:34,600 --> 00:15:36,840 Speaker 1: legged stool of assessing your risk for these long term 292 00:15:37,240 --> 00:15:39,240 Speaker 1: health conditions to try and figure out if you are 293 00:15:39,400 --> 00:15:43,600 Speaker 1: overweight is the b M I plus the waist circumference thing. 294 00:15:44,040 --> 00:15:46,240 Speaker 1: And then also just evaluing your risk factors. Do you 295 00:15:46,280 --> 00:15:49,160 Speaker 1: have things like high blood pressure, how's your cholesterol account, 296 00:15:49,400 --> 00:15:51,320 Speaker 1: how's your blood glu close? Do you have a family 297 00:15:51,400 --> 00:15:54,120 Speaker 1: history of this? Do you smoke? Do you exercise? So 298 00:15:54,440 --> 00:15:57,880 Speaker 1: hopefully we'll be coming to a more well rounded view 299 00:15:57,920 --> 00:16:00,640 Speaker 1: of how we can measure obesity in the future, particularly 300 00:16:00,640 --> 00:16:03,600 Speaker 1: at schools like Lincoln are going to try and dictate 301 00:16:03,640 --> 00:16:07,640 Speaker 1: graduation based on it. And it all goes back to Wheatlet. 302 00:16:08,920 --> 00:16:14,320 Speaker 1: So thanks Wheetlet for all your hard work, but unfortunately 303 00:16:14,360 --> 00:16:20,440 Speaker 1: Mr Queatlet, we have manipulated and misused your tool. But 304 00:16:20,480 --> 00:16:24,520 Speaker 1: maybe we're on the right path now. Maybe maybe maybe 305 00:16:24,600 --> 00:16:26,960 Speaker 1: that should be our health resolution for two thousand and 306 00:16:27,160 --> 00:16:31,160 Speaker 1: en to find a bod pode that's mine. So if 307 00:16:31,200 --> 00:16:33,480 Speaker 1: any of you fair listeners out there have access to 308 00:16:33,520 --> 00:16:36,640 Speaker 1: a bod pod, you should definitely email us at Mom's 309 00:16:36,680 --> 00:16:39,320 Speaker 1: Stuff at how stuff works dot com and make Molly 310 00:16:39,440 --> 00:16:42,640 Speaker 1: Edmonds is year. I don't know if it would make 311 00:16:42,680 --> 00:16:47,000 Speaker 1: my ear well, maybe a week of it. A day, yeah, okay, 312 00:16:47,120 --> 00:16:49,800 Speaker 1: a day make Molly stay with a bod pod And 313 00:16:49,840 --> 00:16:52,080 Speaker 1: speaking of listener mail, why don't we uh whant we 314 00:16:52,120 --> 00:16:57,600 Speaker 1: do some of that? Okay? So today christ we I'm 315 00:16:57,600 --> 00:17:01,000 Speaker 1: gonna read some emails about the women behind Santa Clause, 316 00:17:01,080 --> 00:17:03,920 Speaker 1: that podcast we did right before Christmas. And first off, 317 00:17:03,960 --> 00:17:05,720 Speaker 1: we need to thank many of the listeners. I'm not 318 00:17:05,720 --> 00:17:07,520 Speaker 1: going to identify any of them by name, because a 319 00:17:07,520 --> 00:17:09,920 Speaker 1: lot of people caught us on this one. Um, we 320 00:17:10,240 --> 00:17:14,240 Speaker 1: made fun of the Goody Santa Claus Catherine Lee Bates 321 00:17:14,640 --> 00:17:17,960 Speaker 1: article and uh, it sent me back to eleventh grade 322 00:17:18,000 --> 00:17:20,080 Speaker 1: English as soon as our listeners started writing in. But 323 00:17:20,160 --> 00:17:23,720 Speaker 1: goody is an abbreviation of the title goodwife. So back 324 00:17:23,720 --> 00:17:25,359 Speaker 1: in those days, I would have gone over to have 325 00:17:25,480 --> 00:17:28,000 Speaker 1: been like, Goody, conger, have you any cookies for me? 326 00:17:28,080 --> 00:17:32,080 Speaker 1: Today I would have said no, go home. Then we 327 00:17:32,119 --> 00:17:35,240 Speaker 1: would have burned you as a witch. Um. So yes, 328 00:17:35,320 --> 00:17:38,080 Speaker 1: thanks to all listeners who reminded us of that. And 329 00:17:38,119 --> 00:17:40,119 Speaker 1: now I think we're going to share some theories from 330 00:17:40,160 --> 00:17:44,320 Speaker 1: our listeners about what Mrs Claus's first name is. Yeah, 331 00:17:44,359 --> 00:17:46,760 Speaker 1: George wrote in, and he pointed out that in the 332 00:17:46,800 --> 00:17:52,960 Speaker 1: autobiography of Santa Claus by Jeff gwen Her name is Layla. Alright, 333 00:17:53,119 --> 00:17:57,440 Speaker 1: laws Heather wrote in and said that if you saw 334 00:17:57,480 --> 00:18:00,520 Speaker 1: the nineteen seventy special Santa Claus Is Coming to Town, 335 00:18:01,320 --> 00:18:03,640 Speaker 1: then in that show, it's a girl named Jessica who 336 00:18:03,640 --> 00:18:06,640 Speaker 1: saves Chris Kringel, a young, handsome red hair Santa from 337 00:18:06,680 --> 00:18:10,040 Speaker 1: burger Meister Meister. Burger Burger Meister is mean and cool 338 00:18:10,040 --> 00:18:12,439 Speaker 1: and does not let children have toys. Chris, who has 339 00:18:12,480 --> 00:18:14,359 Speaker 1: a human raised by Elves, gives toys to all the 340 00:18:14,440 --> 00:18:17,400 Speaker 1: kids and yes, a doll to Jessica. They fall in love, 341 00:18:17,600 --> 00:18:19,960 Speaker 1: flee the town, get married, and grew old and happy 342 00:18:20,040 --> 00:18:22,919 Speaker 1: and fat together. Chris learns his real name is Claus 343 00:18:22,920 --> 00:18:26,520 Speaker 1: and becomes Santa Claus. So that makes Mrs Claus Jessica Claus, 344 00:18:26,640 --> 00:18:29,879 Speaker 1: Jessica Claus. We've got Layla Jessica and this one for 345 00:18:30,160 --> 00:18:36,080 Speaker 1: Virginia will round out are a trio of name names, 346 00:18:36,200 --> 00:18:38,679 Speaker 1: and it is my favorite. I thought this was the 347 00:18:38,680 --> 00:18:41,719 Speaker 1: cutest story. We got started to other other suggestions. This 348 00:18:41,760 --> 00:18:47,040 Speaker 1: one just Jase Cake. So basically, Virginia was hanging out 349 00:18:47,080 --> 00:18:50,120 Speaker 1: around Christmas with her boyfriend and his ten year old 350 00:18:50,160 --> 00:18:53,359 Speaker 1: daughter at the time, and his daughter asked her what 351 00:18:53,520 --> 00:18:56,320 Speaker 1: Mrs Claus's first name was and she said, I didn't 352 00:18:56,320 --> 00:18:57,960 Speaker 1: want to tell her that no one had ever thought 353 00:18:58,000 --> 00:19:00,000 Speaker 1: to give Mrs Claus her own name, so I told 354 00:19:00,040 --> 00:19:03,560 Speaker 1: her that Mrs Clause was Merry Christmas, and she decided 355 00:19:03,560 --> 00:19:05,880 Speaker 1: to change her name when she married Santa. I also 356 00:19:05,960 --> 00:19:07,919 Speaker 1: told her that I guess she couldn't have hyphenated her 357 00:19:08,000 --> 00:19:10,280 Speaker 1: name to marry Christmas Clause, but Santa and she had 358 00:19:10,320 --> 00:19:12,760 Speaker 1: been married so long that people really didn't hyphenate names 359 00:19:12,800 --> 00:19:16,240 Speaker 1: back then anyway, So I thought that was pretty durable. 360 00:19:16,320 --> 00:19:19,479 Speaker 1: And if you guys have any emails you would like 361 00:19:19,600 --> 00:19:22,680 Speaker 1: to send our way with your thoughts, feelings, and comments, 362 00:19:23,040 --> 00:19:25,399 Speaker 1: send them on to mom stuff at how stuff works 363 00:19:25,400 --> 00:19:27,760 Speaker 1: dot com. And of course, during the week, you can 364 00:19:27,800 --> 00:19:31,240 Speaker 1: follow us on our blog It's how to stuff, and 365 00:19:31,480 --> 00:19:36,080 Speaker 1: you can learn more about obesity and health and the 366 00:19:36,240 --> 00:19:40,280 Speaker 1: right way to assess your weight. How stuff works dot 367 00:19:40,320 --> 00:19:45,800 Speaker 1: com for more on this and thousands of other topics. 368 00:19:45,840 --> 00:19:49,639 Speaker 1: Because at how stuff works dot com. Want more how 369 00:19:49,720 --> 00:19:52,399 Speaker 1: stuff works, check out our blogs on the house stuff 370 00:19:52,400 --> 00:19:59,080 Speaker 1: works dot com home page. Brought to you by the 371 00:19:59,119 --> 00:20:02,720 Speaker 1: reinvented tooth thousand, twelve Camry. It's ready, are you