Zzzzzz….. – Quantified Self Toronto #14

Sleep is another one of those things like diet, where I feel if you asked anyone if they wanted to improve that area of their life most would say yes.

I remember hearing a quote that sleep is like sex; no one is quite sure how much everyone else is getting, but they are pretty sure it is more than them. Or wait, I think that was salary. With sleep it is more like – no one is quite sure how much they should be getting, but they sure as hell wish they were getting a lot more.

A lot of research has been done on the topic and it seems like the key takeaway from it is always the same: we are not getting enough sleep and this is a problem.

I know that I am a busy guy, that I am young, and that I go out on the weekends, so I know for a fact that my sleep is ‘bad’. But I was curious as to how ‘bad’ it actually is. I started tracking my sleep in April to find out, and also to see if there were any interesting patterns in it of which I was not aware.
 
I spoke again at Quantified Self Toronto (#14) (I spoke previously at #12 on June 7th) about it on August 7th. I gave an overview of my sleep-tracking activities and my simple examination of the data I had gathered. Here is the gist of my talk, as I remember it.

Hi everyone, I’m Myles Harrison and this is my second time speaking at Quantified Self Toronto, and the title of my second presentation is ‘Zzzzzzzz….’. 

I started tracking how much I was sleeping per night starting in April of this year, to find out just how good or bad my sleep is, and also to see if there are any patterns in my sleep cycle.

Now I want to tell you that the first thing I thought of when I started to putting this slide deck together was Star Trek. I remember there was the episode of Star Trek called ‘Deja Q’. Q is an omnipotent being from another dimension that torments the crew of the Enterprise for his own amusement, and in this particular episode he becomes mortal. In one part of the episode he is captured and kept in a cell onboard the ship, and he describes a terrible physical experience he has:

Q
I have been entirely preoccupied by a most frightening experience of my own. A couple of hours ago, I started realizing this body was no longer functioning properly… I felt weak, the life oozing out of me… I could no longer stand… and then I lost consciousness…

PICARD
You fell asleep.

Q
It’s terrifying…. how can you stand it day after day?

PICARD
One gets used to it…


And this is kind of how I have always felt about sleep: I may not like it, there are many other things I’d rather be doing during all those hours, however it’s a necessary evil, and you get used to it. If I could be like Kramer on Seinfeld and try to get by on ‘Da Vinci Sleep’, I probably would. However for me, and for most of the rest of us, that is not a reasonable possibility.

So now we come to the question of ‘how much sleep do we really need?’. Obviously there is a hell of a lot of research which has been done on sleep, and if you ask most people how much sleep they need to get every night, they will tell you something like ‘6-8 hours’. I believe that number comes from this chart which is from the National Sleep Foundation in the States. Here they give the figure of 7-9 hours of sleep for an adult, however this is an average. If you read some of the literature you will find, unsurprisingly, that the amount of sleep needed depends on a lot physiological factors and so varies from person to person. Some lucky people are perfectly capable of functioning normally during the day on only 3 or 4 hours of sleep a night, whereas some other unlucky people really need about 10 to 12 hours of sleep a night to feel fully rested. I highly doubt these unlucky folks regularly get that much sleep a night, as most of us have to get up in the morning for this thing called ‘work’. So yes, these are the extremes but they serve to illustrate the fact that this 6-8 (or 7-9) hours per night figure is an average and is not for everyone.

Also I found a report compiled by Statistics Canada in 2005 which says that the average Canadian sleeps about 8 and a half hours a night, usually starting at about 11 PM. Additionally, most Canadians get about 20 extra minutes of sleep on weekend nights as they don’t have to go to work in the morning and so can hit the snooze button.

So knowing this, now I can look at my own sleep and say, how am I doing and where do I fit in?


So as I said, I have been recording my sleep since early April up until today. In terms of data collection, I simply made note of the approximate time I went to bed and the approximate time at which I woke up the following morning, and recorded these values in a spreadsheet. Note that I counted only continuous night-time sleep and so the data do not include sleep during the day or things napping [Note: this is the same as the data collected by StatsCan for the 2005 report]. Also as a side interest I kept a simple yes/no record of whether or not I had consumed any alcohol that evening, counting as a yes any evening on which I had a drink after 5 PM.

O
n to the data. Now we can answer the question ‘What does my sleep look like?’ and the answer is this:

There does not appear to be any particular rhyme or reason to my sleep pattern. Looking at the graph we can conclude that I am still living like a University student. There are some nights where I got a lot of sleep (sometimes in excess of 11 or 12 hours) and there are other nights where I got very, very little sleep (such as this one particular night in June where I got no sleep at all, but that is another story). The only thing I can really pick out of this graph of note is that following nights or sequences of nights where I got very little sleep or went to bed very late, there is usually a night where I got a very large amount of sleep. Interestingly this night is sometimes not until several days later but this may be due to the constraints of the work week.

So despite the large amount of variability in my sleep we can still look at it and do some simple descriptive statistics and see if we can pull any meaningful patterns out of it. This is a histogram of the number of hours of sleep I got each night.

Despite all the variability in the data from what we saw earlier, it looks like the amount of sleep I get is still somewhat normally distributed. It looks like I am still getting about 7 hours of sleep on average, which actually really surprised me and in my opinion is quite good, all things considered and given the chaotic nature of my personal life. [Note: the actual value is 6.943 hrs for the mean, 7 for the median with a standard deviation of 1.82 hours]. 

So we can ask the question, ‘Is my amount of nightly sleep normally distributed?’. Well, at first glance it sure appears like it might be. So we can compare to what the theoretical values should be, and this certainly seems to be the case, though using a histogram is maybe not the best way as it will depend on how you choose your bin sizes.


We can also look at what is called a Q-Q plot which plots the values against the theoretical values, and if the two distributions are the same then the values should lie along that straight line. They do lie along it well, with maybe a few up near the top there straying away… so perhaps it is a skew-normal distribution or something like that, but we can still safely say that the amount of sleep I get at night is approximately normally distributed.


Okay, so that is looking at all the data, but now we can also look at the data over the course of the week, as things like the work week and weekend may have an affect on how many hours of sleep I get.

So here is a boxplot of the number of hours of sleep I got for each day of the week and we can see some interesting things here.

Most notably, Wednesday and Saturday appear to be the ‘worst’ nights of the week for me for sleep. Saturday is understandable, as I tend to go out on Saturday nights, and so the large amount of variability in the number of hours and low median value is to be expected; however, I am unsure as to why Wednesday has less hours than the other days (although I have do go out some Wednesday nights). Tuesdays and Thursdays appears to be best both in terms of variability and the median amount, these days being mid-week where presumably my sleep cycle is becoming regular during the work week (despite the occasional bad Wednesday?).

We can also examine when I feel asleep over the course of the week. Wait, that sounds bad, like I am sleeping at my desk at work. What I mean is we can also examine what time I went to bed each night over the course of the week:

Again we can see some interesting things. First of all, it is easy to note that on average I am not asleep before 1 AM! Secondly we can see that I get to sleep latest on Saturday nights (as this is the weekend) and that there is a large amount of variability in the hour I fall asleep on Fridays. But again we see that in terms of getting to bed earliest, Wednesday and Saturday are my ‘worst’ days, in addition to being the days when I get the least amount of sleep on average. Hmmmmmm….! Could there be some sort of relationship here?

So we can create a scatterplot and see if there exists a relationship between the hour at which I get to bed and the number of hours of sleep I get. And when we do this we can see that there is appears to be [surprise, surprise!] a negative correlation between the hour at which I get to sleep and the number of hours of sleep I get.

And we can hack a trend line through there to verify this:

> tl1 <- lm(sleep$hours ~ starthrs)
> summary(tl1)

Call:
lm(formula = sleep$hours ~ starthrs)

Residuals:
    Min      1Q  Median      3Q     Max
-7.9234 -0.6745 -0.0081  0.5569  4.8669

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  9.78363    0.43696  22.390  < 2e-16 ***
starthrs    -0.62007    0.09009  -6.883 3.56e-10 ***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.533 on 112 degrees of freedom
Multiple R-squared: 0.2973,    Adjusted R-squared: 0.291
F-statistic: 47.38 on 1 and 112 DF,  p-value: 3.563e-10

So there is a highly statistically significant relationship between how late I get to sleep and the number of hours of sleep I get. For those of you that are interested, the p-value is very small (on the order of e-10). However you can see that the goodness of fit is not that great, as the R-squared about 0.3. This means that perhaps there are other explanations as to why getting to sleep later results in me getting less sleep, however I could not immediately think of anything. I am open to other suggestions and interpretations if you have any.

Also I got to thinking that this is the relationship between how late I get to sleep and how much sleep I get for all the data. Like a lot of people, I have a 9 to 5, and so I do not have the much choice about when I can get up in the morning. Therefore I would expect that this trend is largely dependent upon the data from the days during the work week.

So I thought I would do the same examination only for the days of the week where the following day I do not have to be up by a certain hour, that is, Friday and Saturday nights. And we can create the same plot, and:

We can see that, despite there being less data, there still exists the negative relationship.

> tl2 <- lm(wkend$hours ~ hrs)
> summary(tl2)

Call:
lm(formula = wkend$hours ~ hrs)

Residuals:
    Min      1Q  Median      3Q     Max
-5.4288 -0.4578  0.0871  0.5536  4.4300

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  12.1081     0.9665  12.528 1.89e-13 ***
hrs          -0.8718     0.1669  -5.224 1.24e-05 ***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.737 on 30 degrees of freedom
Multiple R-squared: 0.4764,    Adjusted R-squared: 0.4589
F-statistic: 27.29 on 1 and 30 DF,  p-value: 1.236e-05

So it appears that on the days on which I could sleep in and make up the hours of sleep I am losing by going to bed later I am not necessarily doing so. Just because I can sleep in until a ridiculously late hour doesn’t necessarily mean that my body is letting me do so. This came as a bit of a surprise to me, as I thought that if I didn’t have to be up at a particular hour in the morning to do something, I would just sleep more to make up for the sleep I lost. An interesting insight – even though I can sleep in and make up for hours lost doesn’t necessarily mean that I will. 

So basically I just need to get to sleep earlier. Also, I am reminded of what my Dad always used to say to me when I was a kid, ‘An hour of sleep before midnight is worth two afterwards.’

Lastly, as I said, I did keep track of which nights I had consumed any alcohol in the evening to see what impact, if any, this was having on the quality and duration of my sleep. For this I just did a simple box plot of all the data and we can see that having a drink does mean I get less sleep overall.


Though this is a very simple overview it is consistent with what you can read in the research done on alcohol consumption and sleep. The belief that having a drink before bed will help you sleep better is a myth, as alcohol changes physiological processes in the body which are necessary for a good night’s sleep, and disrupts it.


So those were the conclusions I drew from tracking my sleep and doing this simple analysis of it. In terms of future directions, I could also further quantify my tracking of my sleep. I have simply measured the amount of sleep I have been getting, going with the assumption that getting close to the recommended amount of time is better. I could further quantify things by rating how rested I feel when I awake (or during the day) or rating how I felt the quality of rest I got was, on a scale of 1-10.

I could also measure other factors, such eating and exercise, and the time these things occur and how this play in to the amount and quality of the sleep I get.

Lastly, though I did have a simple yes/no measurement for whether or not I had consumed alcohol each evening, I did not quantify this. In the future I could measure caffeine consumption as well, as this known to be another important factor affecting sleep and restfulness.

That concludes my presentation, I hope I kept you awake. I thank you for your time, and for listening. If you have any questions I would be happy to answer them.

References & Resources 

National Sleep Foundation
http://www.sleepfoundation.org/

Who gets any sleep these days? Sleep patterns of Canadians (Statistics Canada)
http://www.statcan.gc.ca/pub/11-008-x/2008001/article/10553-eng.htm

The Harvard Medical School’s Guide to A Good Night’s Sleep
http://books.google.ca/books?id=VsOWD6J5JQ0C&lpg=PP1&pg=PP1#v=onepage&q&f=false

Quantified Self Toronto
http://quantifiedself.ca/

How much do I weigh? – Quantified Self Toronto #12

Recently I spoke at the Quantified Self Toronto group (you can find the article on other talk here).

It was in late November of last year that I decided I wanted to lose a few pounds. I read most of The Hacker’s Diet, then began tracking my weight using the excellent Libra Android application. Though my drastic reductions of my caloric intake are no more (and so my weight is now fairly steady) I continue to track my weight day-to-day and build the dataset. Perhaps later I can do an analysis of the patterns in fluctuations in my weight separate from the goal of weight loss.

What follows is a rough transcription of the talk I gave, illustrated by the accompanying slides.

Hello Everyone, I’m Myles Harrison and today I’d like to present my first experiment in quantified self and self-tracking. And the name of that experiment is “How Much Do I Weigh?”

So I want to say two things. First of all, at this point you are probably saying to yourself, “How much do I weigh? Well, geez, that’s kind of a stupid question… why don’t you just step on a scale and find out?” And that’s one of the things I discovered as a result of doing this, is that sometimes it’s not necessarily that simple. But I’ll get to that later in the presentation.

The second thing I want to say is that I am not fat.

However, there are not many people whom I know where if you ask them, “Hey, would you like to lose 5 or 10 pounds?” the answer would be no. The same is true for myself. So late last November I decided that I wanted to lose some weight and perhaps get into slightly better shape. Being the sort of person I am, I didn’t go to the gym, I didn’t go a personal trainer, and I didn’t meet with my doctor to discuss my diet. I just Googled stuff. And that’s what lead me to this

The Hacker’s Diet, by John Walker. Walker was one of the co-founders of the company Autodesk which created the popular Autocad software and later went on to become a giant multinational company. Mr. Walker woke up one day and had a realization. He realized that he was very successful, very wealthy, and had a very attractive wife, but he was fat. Really fat. And so John Walker though, “I’ve used my intelligence and analytical thinking to get all these other great things in my life, why can’t I apply my intelligence to the problem of weight, and solve it the same way?” So that’s exactly what he did. And he lost 70 pounds.

Walker’s method was this. He said, let’s forget all about making this too complicated. Let’s look at the problem of health and weight loss as an engineering problem. So there’s just you:

and your body is the entire system, and all this system has, the only things we’re going to think about are inputs and outputs. I don’t care if you’re eating McDonald’s, or Subway, or spaghetti 3 times a day. We’re just talking about the amount of input – how much? Therefore, from this incredibly simplified model of the human body, the way to lose weight is just to ensure that the inputs are less than the outputs.

IN < OUT

Walker realized that this ‘advice’ is so simple and obvious that it is nearly useless in itself. He compared it to the wise financial guru, on being asked how to make money on the stock market by an apprentice, giving the advice: “It’s simple, buy low and sell high.” Still, this is the framework we have as a starting point, so we proceed from here.

So now this raises the question, “Okay well how do we do that?” Well, this is a Quantified Self meet up, so as you’ve probably guessed, we do it by measuring.

We can measure our inputs by counting calories and keeping track of how much we eat. Measuring output is a little more difficult. It is possible to approximate the number of calories burned when exercising, but actually measuring how much energy you are using on a day-to-day basis, just walking around, sitting, going to work, sleeping, etc. is more complicated, and likely not practically possible. So instead, we measure weight as a proxy for output, since this is what we are really concerned with in the first place anyhow. i.e. Are we losing weight or not?

Okay, so we know now what we’ve got to do. How are we going to keep track of all this? Walker, being a technical guy, suggests entering all the information into a piece of computer software, oh, say, I don’t know, like a certain spreadsheet application. This way we can make all kinds of graphs and find the weighted moving average, and do all kinds of other analysis. But I didn’t do that. Now don’t get me wrong, I love data and I love analyzing it, and so I would love doing all those different types of things. However, why would I use a piece of software that I hate (and am forced to on a regular basis) any more than I already have to? Especially when this is the 21st century and I have a perfectly good smartphone and somebody already wrote the software to do it for me!

So, I’m good! Starting in late November of last year I followed the Hacker’s Diet directions and weighed myself every day (or nearly every day, as often as I could) at approximately the same time of day. And along the way, I discovered some things.

One day I was at work and I got a text from my roommate, and it said “Myles, did you draw a square on the bathroom floor in black permanent marker?” To which I responded, “Why yes I did.” To which the response was “Okay, good.” And the reason I that I drew a square on the tiles of the bathroom floor in black permanent marker was because of observational error. More specifically, measurement error. 

If you know anything about your typical drugstore bathroom scale you probably know that they are not really that accurate. If you put the same scale on an uneven surface (say, like tiles on a bathroom floor) you can make the same measurement back-to-back and get wildly different values. That is to say the scales have a lot of random error in their measurement. And that’s why I drew that square on the bathroom floor. That was my attempt to control measurement error, by placing the scale in as close to the same position I could every morning when I weighed myself. Otherwise you get into this sort of bizarre situation where you start thinking, “Okay, so is the scale measuring me or am I measuring the scale?” And if we are attempting to collect some meaningful data and do a quantified self experiment, that is not the sort of situation we want to be in.

So I continued to collect data from last November up until today. And this is what it looks like.

As you can see like most dieters, I was very ambitious at the start and lost approximately 5 pounds between late November and and the tail end of December. That data gap, followed by a large upswing corresponds to the Christmas holidays, when I went off my diet. After that I continued to lose weight, albeit somewhat more gradually up until about mid-March, and since then I have ever-so-slowly been gaining it back, mostly due to the fact that I have not been watching my input as much as I was before.

So, what can we take away from this graph? Well, from my simple ‘1-D’ analysis, we can see a couple of things. The first thing, which should be a surprise to no one, is that it is a lot easier to gain weight than it is to lose it. I think most everyone here (and all past dieters) already knew that. 

Secondly, my diet aside, it is remarkable to see how much variability there is in the daily measurements. True, some of this may be due to the aforementioned measurement error, however in my readings online I also found that a person’s weight can vary by as much as 1 to 3 pounds on a day-to-day basis, due to various biological factors and processes.

Walker comments on this variability in the Hacker’s Diet. It is one of his reasons as to why looking at the moving average and weighing oneself every day is important, if you want to be able to really track whether or not a diet is working. And that’s why doing things like Quantified Self are important, and also what I was alluding to earlier when I said that the question of “How much do I weigh?” is not so simple. It’s not simply a matter of stepping on the scale and looking at a number to see how much you weigh. Because that number you see varies on a daily basis and isn’t a truly accurate measurement of how much you ‘really’ weigh.

!

This ties into the third point that I wanted to draw from the data. That point is that the human body is not like a light switch, it’s more like a thermostat. I remember reading about a study which psychologists did to measure people’s understanding of delayed feedback. They gave people a room with a thermostat, but there was a delay in the thermostat, and it was set to something very very high, on the order of several hours. The participants were tasked with getting to room to stay at a set temperature, however none of them could. Because people (or most people, anyhow) do not intuitively understand things like delayed feedback. The participants in the study kept fiddling with the thermostat and setting it higher and lower because they thought it wasn’t working, and so the temperature in the room always ended up fluctuating wildly. The participants in the study were responding to what they saw the temperature to be when they should have been responding to what the temperature was going to be.

And I think this is a good analogy for the problem with dieting and why it can be so hard. This is why it can be easy to become frustrated and difficult to tell if a diet is working or not. Because if you just step on the scale every day and look at that one number, you don’t see the overall picture, and it can be hard to tell whether you’re losing weight or not. And if you just see that one number you’d never realize that though I can eat a pizza today and I will weight the same tomorrow, it’s not until 3 days later that I have gained 2 pounds. It’s a problem of delayed feedback. And that’s one of the really interesting conclusions I came to ask a result of performing this experiment.

So where does this leave us for the future?

Well, I think I did a pretty good job of measuring my weight almost every day and was able to make some interesting conclusions from my simple ‘1-D’ analysis. However, though I did very well tracking all the output, and did not track any of my inputs whatsoever. In the future if I kept track of this as well (for instance by counting calories) I would have more data and be able to draw some more meaningful conclusions about how my diet is impacting my weight.

Secondly, I did not do one other thing at all. I didn’t exercise. This is something Walker gets to later in his book too (like most diet/health books) however I did not implement any kind of exercise routine or measurement thereof.

In the future I think if I implement these two things, as well as continuing with my consistent measurement of my weight, then perhaps I could ‘get all the way there’

 

|—————| 100%

 
That was my presentation, thank you for listening. If you have any questions I will be happy to answer them.

References / Resources

Libra Weight Manager for Android
https://play.google.com/store/apps/details?id=net.cachapa.libra 

The Hacker’s Diet
http://www.fourmilab.ch/hackdiet/www/hackdiet.html 

Quantified Self Toronto
http://quantifiedself.ca/ 

I’m Lovin’ It? – A Nutritional Analysis of McDonald’s

Introduction

The other day I ate at McDonald’s.

I am not particularly proud of this fact. But some days, you are just too tired, too lazy, or too hung-over to bother throwing something together in the kitchen and you just think, “Whatever, I’m getting a Big Mac.”

As I was standing in line, ready to be served a free smile, I saw that someone had put up on the wall the nutritional information poster. From far away I saw the little columns of data, all in neatly organized tabular form, and a light went on over my head. I got excited like the nerd I am. Look! Out in the real world! Neatly organized data just ready to be analyzed! Amazing!

So, of course, after finishing my combo #1 and doing my part to contribute to the destruction of the rain forest, the world’s increasingly worrying garbage problem, and the continuing erosion of my state of health, I rushed right home to download the nutritional information from Ronald McDonald’s website and dive into the data.

Analysis

First of all, I would just like to apologize in advance for
a) using a spreadsheet application, and
b) using bar charts

Forgive me, but we’re not doing any particularly heavy lifting here. And hey, at least it wasn’t in that one piece of software that everybody hates.

Also, by way of a disclaimer, I am not a nutritionist and the content of this article is in no way associated with McDonald’s restaurants or Health Canada.

Sandwiches
First things first. Surprisingly, the largest and fattiest of the items on the board is (what I consider to be) one the “fringe” menu items: the Angus Deluxe Burger. Seriously, does anybody really ever order this thing? Wasn’t it just something the guys in the marketing department came up to recover market share from Harvey’s? But I digress.

Weighing in at just a gram shy of 300, 47 of which come from fat (of which 17 are saturated) this is probably not something you should eat every day, given that it has 780 calories. Using a ballpark figure of 2000 calories a day for a healthy adult, eating just the burger alone would make up almost 40% of your daily caloric intake.

 
Unsurprisingly, the value menu burgers are not as bad in terms of calories and fat, due to their smaller size. This is also the case for the chicken snack wraps and fajita. The McBistro sandwiches, though they are chicken, are on par with the other larger burgers (Big Mac and Big Xtra) in terms of serving size and fat content, so as far as McD’s is concerned choosing a chicken sandwich is not really a healthier option over beef (this is also the case for the caloric content).

As the document on the McDonald’s website is a little dated, some newer, more popular menu items are missing from the data set. However these are available in the web site’s nutritional calculator (which unfortunately is in Flash). FYI the Double Big Mac has 700 calories and weighs 268 grams, 40 of which come from fat (17 saturated). Close, but still not as bad as the Angus Deluxe.

In terms of sodium and cholesterol, again our friend the Angus burger is the worst offender, this time the Angus with Bacon & Cheese, having both the most sodium and cholesterol of any burger on the menu. With a whopping 1990 mg of sodium, or approximately 80% of Health Canada’s recommended daily intake, that’s a salty burger. Here a couple of the smaller burgers are quite bad, the Double Cheeseburger and Quarter Pounder with Cheese both having marginally more sodium than the Big Mac as well as more cholesterol. Best stick with the snack wraps or the other value menu burgers.

Fries
Compared to the burgers, the fries don’t even really seem all that bad. Still, if you order a large, you’re getting over 40% of your recommended daily fat intake. I realize I’m using different units than before here, so for your reference the large fries have 560 calories, 27 grams of fat and 430 mg of sodium.

Soft Drinks
If you are trying to be health-conscious, the worst drinks you could possibly order at McDonald’s are the milkshakes. Our big winner in the drinks department is the large Chocolate Banana Triple Thick Milkshake®. With a serving size of 698g (~1.5 lbs), this delicious shake has over 1000 calories and nearly 30 grams of fat. In fact the milkshakes are, without question, the most caloric of all the drinks available, and are only exceeded in sugar content by some of the large soft drinks.

In terms of watching the calories and sugar, diet drinks are your friend as they have zero calories and no sugar. Below is the caloric and sugar content of the drinks available, sorted in ascending order of caloric content.

 

Salads
And now the big question – McDonald’s salads: a more conscientious choice, or another nutritional offender masquerading as a healthy alternative?

There are quite healthy alternatives in the salad department. Assuming you’re not going to order the Side Garden Salad (which I assume is just lettuce, looking at its caloric and fat content) the Spicy Thai Salad and Spicy Thai with Grilled Chicken are actually quite reasonable, though the latter has a large amount of sodium (520 mg), and all the Thai and Tuscan salads have a lot of sugar (19 and 16 grams of sugar respectively).

However, all these values are referring to the salads sans dressing. If you’re like me (and most other human beings) you probably put dressing on your salad.

The Spicy Thai Salad with the Asian Sesame Dressing added might still be considered within the realm of the healthy – totaling 250 calories and 11 grams of fat. However, keep in mind that would also have 530 mg of sodium (about a quarter of the recommended daily intake) and 29 grams of sugar. Not exactly health food, but not the worst thing you could order.

And for the love of god, just don’t order any old salad at McD’s and think you are making a healthy alternative choice. The Mighty Caesar with Crispy Chicken and Caesar dressing has more fat than a Big Mac combo with medium fries and a Coke (54 g vs. 46 g) and nearly as much sodium (1240 mg vs. 1300 mg), over half the daily recommended intake.

Conclusions

Doing this brief simple examination of the McDonald’s menu will definitely help me be more mindful about the food the next time I choose to eat there. However in terms of of take-aways, there is nothing here really too surprising – we can see that McDonald’s food is, in general, very high in calories, fat, sugar and sodium. This is probably not a surprise for most, as many continue to eat it while being aware of these facts, myself included.

Still, it is somewhat shocking to see it all quantified and laid out in this fashion. A Big Mac meal with a medium fries and medium coke, for instance, has 1120 calories, 46 grams of fat, 1300 mg of sodium and 65 grams of sugar. Yikes. Assuming our 2000 calorie diet, that’s over half the day’s calories in one meal, as well as 71% and 54% of the recommended daily values for fat and sodium respectively. I will probably think twice in the future before I order that again.

If you are trying to be health-conscious and still choose to eat underneath the golden arches, based on what we have seen here, some pointers are:

  • Avoid the Angus Burgers
  • Order a smaller burger (except the double cheese), snack wrap or fajita
  • Avoid the milkshakes
  • Drink diet soft drinks
  • Some salads are acceptable, Caesar dressing is to be avoided

References / Resources

McDonald’s Nutritional Information
http://www1.mcdonalds.ca/NutritionCalculator/NutritionFactsEN.pdf

McDonald’s Canada Nutritional Calculator
http://www.mcdonalds.ca/ca/en/food/nutrition_calculator.html

The Daily % Value (Health Canada)
http://www.hc-sc.gc.ca/fn-an/label-etiquet/nutrition/cons/dv-vq/info-eng.php

Dietary Reference Intake Tables, 2005 (Health Canada)
http://www.hc-sc.gc.ca/fn-an/nutrition/reference/table/index-eng.php

LibreOffice Calc
http://www.libreoffice.org/