Technically Minded – Episode 2: How to live longer

A new podcast episode – What is and what is not important to living longer—with science!

Be sure to subscribe through the iTunes store or add http://idolanalytics.com/feed/podcast/ to your favorite podcasting app.

Links:

Shepard Smith on Ebola
The true story of Stronzo Bestiale
Annals of Internal Medicine statement about Vitamins
Annals of Internal Medicine review of 240 papers on organic food

Data on Loss of Life Expectancy:

Data from Bernard Cohen’s 1979 paper
Cohen’s 1991 paper (full text pdf, free). This paper explains the methodology in full of how the LLE was calculated.

Loss of Life Expectancy (LLE) due to various behaviors. All data taken from Cohen 1979[1] and 1991[2], except where indicated.

Activity or risk LLE (days)
Smoking cigarettes (male) 2300
30% overweight 1300
Working as a coal miner 1100
20% overweight 900
Smoking cigarettes (female) 800
Sub-optimal medical care 550
10% overweight 450
Vietnam army service 400
Driving (man) 360
Living in Southeast (SC,MS,GA,LA,AL) 350
Smoking cigars 330
Working in mining construction 320
Drinking 130-230
Salt intake[3] 220
Smoking pipes 220
Driving (men & women) 207
Driving (women) 150
Avid mountain climbing 110
Drug abuse (legal drugs) 90-100
Diabetic 95
Air pollution 80
Occupational accidents 74
Small cars (vs. midsize) 60
Married to smoker 50
Drowning 40
Speed limit: 65 vs. 55 miles per hour 40
Falling down 39
Walking/Pedestrian 37
Poison + suffocation + asphyxiation 37
Radon in homes 7-35
Safe job 30
Coffee: 2 cups/day 6-26
Radiation worker, age 18-65 25-40
Illegal drugs 18
Firearms 11
Getting regular X-rays 7
Biking 5
Birth control pills 5
Hurricanes, tornadoes 1
Flying 1
Living near a dam 1
Living near nuclear plant 0.4
Getting a regular PAP -4
1 hour vigorous activity/day for a year[4] -40
[1] Health Physics, Volume 36, Number 6, June 1979 36, Pages 707-722
[2] Health Physics, Volume 61, Number 3, September 1991, Pages 317-335
[3] New England Journal of Medicine, Volume 371, Issue 7, August 2014, Pages 624-634
[4] American Journal of Preventive Medicine, Volume 44, Issue 1, January 2013, Pages 23–29

Technically Minded: Episode 1

The first episode of my new podcast. Here I talk about what this show is all about, a slightly different view of news, culture, health, and art than you get from many other sources. This week I talk about science in journalism, including what I feel is the main problem with how journalists approach the news. I also comment on the Princeton Election Consortium’s “model” of this year’s midterm election.

How many calories do I need?: An empirical approach

It’s been said many times that weight loss isn’t easy but it is simple. Overall this is a pretty true statement. If you eat a certain number of calories per day, and you want to lose weight, you can reduce this number and, as long as your level of activity stays the same, you will probably lose weight. To get an idea of how many calories are significant, there are a couple of formulas that people tend to use. The first is the number of calories that your body needs, which is stated by many sources as

BMR = 10 * weight(kg) + 6.25 * height(cm) – 5 * age(y) + 5 (man)
BMR = 10 * weight(kg) + 6.25 * height(cm) – 5 * age(y) – 161 (woman)

where BMR is the basal metabolic rate, the number of calories that your body needs to survive. To get your full caloric need, you are then instructed to add a number of calories corresponding to your degree of daily activity

Total needed = BMR + activity.

Now, both of these numbers are highly uncertain for an individual. This is a common problem for health-related sciences, where they have to quantify a huge population of diverse people with an average quantity. For instance, it’s normally assumed that Body Mass Index for a person indicates how fat the person is, but in fact that relationship is highly imperfect:

BMIvsBFDepending on your body type, your BMR might be several hundred calories less than the formula or several hundred more. And estimating your activity level is at least as uncertain, since it will vary based on how much weight you lift, how much cardio you do, and how much walking around you do in your job. All of these things are logical, but they aren’t trivial to quantify.

So I set out to determine what my actual caloric need was, which was kind of a side issue to the fact that I wanted to cut my body fat percentage (call it Justin-Theroux-envy after watching The Leftovers this summer). In my view, what people mostly want to accomplish in a weight loss program is not weight loss per se, but a drop in body fat percentage. For men, this is accompanied by a desire to simultaneously maximize muscle mass, which is somewhat independent from body fat percentage (that is, you can have a very low body fat percentage while still not having very much muscle, like Iggy Pop, and you can also be very muscular but still have a high percentage of body fat, like most NFL linebackers). There is only one scientific fact I use: that a pound of fat is 3500 calories. You might think that this is as unsure as all the other numbers, but in fact I think not—a pound of fat can be put into a bomb calorimeter and the heat released in it measured precisely. So I have every confidence that number is correct.

Here is a method that seems to work pretty well.

Step 1: Measure your body fat percentage.
Step 2: Diet and exercise while collecting weight and caloric intake data every day for a few months.
Step 3: Measure body fat percentage at regular intervals (say, once a month)
Step 4: Determine true fat loss and compare with how many calories you’ve cut
Step 5: Determine the actual number of calories you’ve cut, therefore telling you your true caloric need.

Once these steps are complete, we can calculate our individual body’s caloric need.

Step 1: Measure your body fat percentage

Measuring body fat percentage isn’t all that difficult, and with calipers it’s not that expensive either. A necessary caliper can be bought from Amazon for about $14. Then, follow the directions on this site for where to measure: The formulas given on the page are straightforward to apply, in my Excel file I had, for example

Triceps Pectoral Abdomen Supra-
iliac
Thigh Density % Body fat
7/9/2014 17 14 23 20 12 1.038 25.9
9/1/2014 10 8 17 17 12 1.052 20.1

I took data only after large changes. The measurement itself is a bit cumbersome to do alone, and the idea of pinching my skin every day with those calipers is not appealing. The measurement itself is about +/- 2% when done correctly. Also, be aware that for extremes of body fat this method is not nearly as effective. My presumption is that if I manage to get down to about 10% body fat (unlikely), I will need to visit a bod-pod facility to have my body fat measured professionally. However, in my regime (15-25%), it’s fine.

Step 2: Diet and exercise

It’s really irrelevant how much you decide to cut out of your diet. I estimated based on the BMR and activity that I needed about 2400 calories per day (this turned out to be wrong). I therefore resolved to eat between 1200 and 1400 calories each day, for around a 1000 calorie per day deficit. I also started working out every day, two days lifting and one day cardio. Again, it’s not rocket science. My weight over time looked like this:

weightvstimeObviously the weight you are has a bit of variance from day to day, based on the amount of water in your body and the amount of food still in your stomach and intestines. It’s a good idea to collect the data every day and do a regression fit like this to give a good estimate of your true weight. I also recommend weighing yourself at the same time and under the same circumstances every day. In my case, it was after working out, shoes off, at night.

As for calorie counting, I used the iPhone myfitnesspal app most of the time. Every day I entered my caloric intake into a spreadsheet along with the date and my weight.

Step 3: Determining your actual caloric rate

Since I went from 25% to 20% fat between those two dates, the lean weight gained is

lean weight gained = 0.8 current weight – 0.75 previous weight = 0.7 pounds

Here’s where we make our first correction to caloric need. A deficit of 1000 calories, especially with an increase in my exercise, should have yielded a fat loss of 7000/3500 = 2 pounds a week. Between the two dates that I measured my body fat, my actual body fat percentage dropped 5% and my weight had dropped 9 pounds. That was over 8 weeks exactly, so while I thought I would lose 16 pounds of fat, I actually lost 9.7 pounds of fat. This implies that my caloric deficit was actually 600 calories per day and not 1000. Clearly, the formula predicting 2400 calories was not right for me. And, in fact, my body needs just 2000 calories to maintain weight.

This process should be done iteratively, and I carried out this procedure on a longer time span. Assuming this is right, by now (9/28/2014) I have gained about 1.67 pounds of lean weight and lost 13.2 pounds of total weight, making my total fat loss 14.9 pounds. This predicts a body fat percentage of 16%, whereas a caliper measurement today determines it as 17% (not bad!). Making the assumption that the weight went on evenly, we can plot our weight versus our calorie deficit (divided by 3500) assuming that my “activity” is 369 calories per day:

deficitvsweightlossWith a slope of 1, this now appears to be a correct determination of my metabolic rate (both basal and active). At my present weight I need 1890 calories per day to maintain weight, a far cry from the equation commonly cited, which I thought would predict my rate at 2400 calories. In reality, I was probably overestimating my “activity” by a few hundred calories, and likely my job is too sedentary to fit with the average BMR as well.

But actually it doesn’t matter what the cause is. This process is iterative and totally empirical, and so far as I can tell is a quite accurate way to determine your necessary caloric intake. Now note that even this 1890 calories assumes that my workout plan stays the same. If I start lifting heavier it might go up, and if I slack off it might go down. In particular, I would recommend that any time you see a kink in your weight versus time graph, you start recalculating. That hasn’t happened to me yet.

Here’s the Excel file with all the formulas and with my data. You can use it to enter your own information and determine your own caloric rate.

Shameless plug: My book is on Amazon

Just a quick off-season note: I haven’t been blogging much at all, but I did put all the finishing touches on my book, Thermo for Normals: Everyday Thermodynamics for non-scientists. If you are a regular reader of this site, you have the technical expertise to read and understand it, as it takes a look at thermodynamics from an accessible perspective. Here’s the link: Thermo for Normals