Posts tagged with weight loss

@tdhopper posted his self-measurements of weight loss


a few months back. I recently decided also that I wanted to lose fat-weight—the infamous “I could stand to be a few kilos lighter”—and I think I came up with a more productive way of thinking about my progress: I’m not going to look at the scale at all. I’m just going to count calorie estimates from the treadmill estimator or use online calculators for how much is burned by running / swimming — and calories burned is the only thing I will use: no attempts at eating less.


Also, instead of thinking in terms of weight I’m going to think in terms of volume. Here are some pictures of people holding 5 pounds of fat (2¼ kilos):

As you can see this is a large fraction of a person’s flesh, if their BMI is in the healthy range.

I’m not so fat that I have tens of litres of fat making up my body. Rather if I look at myself and visually “remove 2 litres” that “looks” like it would be very substantial—such a huge volume that, of course it would take weeks of diligent exercise!

But as we know from Mr Hopper’s posts (or I know it from my own experience of weighing myself), the noise is louder than the signal.

The magnitude of daily variation swamps the magnitude of “fundamental” progress.


The goal of counting kcal burned and thinking in terms of volume is to make both the goals and the progress feel more visceral. Everybody knows how to lose weight, the problem is just that one doesn’t do it. Other than simply increasing self-discipline or increasing the mental energy I put towards this goal (neither of which I want to do).

  1. More accurate measurement of my small-scale progress and
  2. Choosing meaningful goals in the first place—not a number grabbed out of the air (“five kilos”—why five?), but rather imagine how much volume has left my muffin-top and how much volume is left—whilst still carrying with me the “larger numbers” associated with kcal fat-loss, than the “small numbers” which characterise litres (gallons ~ 8 lbs) of fat loss.

Here’s my mathematical model of why this is hard in the first place:

  • I take about 100 measurements at roughly the same time but not exactly timepoints <- 1:1e2 + rnorm(1e2,sd=1)
  • the natural variation in weight, in the unit scale of [kcal stored by fat] is on the order of kilos daily.variation <- 1e5 * sin( runif(1,min=-pi/2,max=pi/2) + timepoints)
  • even if I subtracted off my daily fluctuation pattern (Mr Hopper does this by weighing himself at the same time every day), there are apparently other noise factors on the order of half a kilo or perhaps .1 kilo other.variation <- 1e4 * sin( runif(1,min=-pi/2,max=pi/2) + timepoints)

  • the “underlying phenomenon” I’m trying to measure is perhaps on the order of .01 kilos lost per day. Let’s say I lose 1 kilo in 3 weeks, that would be 8000 kcal if I’m good. (i.e., I actually do my workouts and I don’t eat a compensatory extra 8000± kcal). I could model the underlying fat loss as a step function to be more truthful but I’ll use a linear model, saying I lose 100 kcal per measurement (supposing I measure 3 times a day) rather than 700 kcal every time I work out, which is not once a day (that would be the step function). But the catch is, I’m not sure if I’m compensating by eating more. My statistical task is to estimate B, in other words to distinguish if I’m losing weight or not, and how fast I’m losing it (in kcal units, leaving the conversion 8000 kcal ~ 1 kilo as an afterthought), from the signal-swamped data. B<-rnorm(1,mean=100,sd=50); trend<-−B*timepoints
  • Now my job is to estimate B. Is it even positive? (i.e. am I actually losing weight?) In R I just made the variable so I could print(B) but the point is to model why it’s hard to do this from my real data, which is the sum data <- daily.variation   +   other.variation   - B*timepoints
  • This is why I like my idea: measurements of kcal burned on the treadmill is 1000 times more precise than measurements of my bodyweight.

So my overall system is to do “chunks” of 7000 kcal = 1 kilo of fat or 3500 kcal =1 pound of fat. I can stand to do 500–700 kcal per cardio session—about an hour. (I also do an extra +1 kcal for every minute it took me to penalise for low speed: exercise crowds out normal metabolism.) Then it becomes a “long count” up to 3500 or up to 7000. That means 5 cardio sessions (of 770 kcal each) to get up to 1 pound of fat-loss, 7 wimped-out cardio sessions (of 550 kcal each) to reach a pound, and so on. It’s easy enough to “count to 5”. This system makes each one of the 5 be significantly large at the order of magnitude appropriate to convert kcal of exercise to litres of body volume.

Whilst reading John Hempton’s post on shorting $HLF I decided to follow along in quantmod.

Long story short, HerbaLife sells weight-loss supplements through a multilayer marketing business model which Bill Ackman contends is an unsustainable, illegal pyramid scheme. Ackman calls it “the only billion-dollar brand no-one’s ever heard of” and Hempton posts some very unflattering Craigslist marketing adverts:




thus undermining its credibility.

I should mention that I got some internet ads from Pershing Square Capital Management when I googled for herbalife information. In other words the shorts are spreading the word around to the common man to jump on this short! Destroy this pyramid scheme! You could read this as similar to a penny-stock email, but I view it simply as credible self-interest: I already put my shorts on for what I believe are rational reasons. It’s obviously in my self-interest to convince you to do the same but I do in fact believe that $HLF will and should go down and you know I do because I put my money where my mouth is. Whether that’s an ideal confluence of honesty, moral high ground, and selfishness—capitalism at its best—or some overpowerful hedgies using their marketing dollars to bring down a solid company, I’ll leave up to you.


Anyway, on to the quantmod stuff.

Here’s how to generate the 2007–present view:


require(quantmod); getSymbols('HLF'); setDefaults(chartSeries, up.col="gold", dn.col="#2255aa", color.vol=FALSE); chartSeries(HLF)

Now here’s the interesting part.

(…”Ackman” should read “Einhorn” in red there…)

You can notice in red that trades per day (volume) have risen to 10, 20 times normal levels during 2013—which maybe we can attribute to the “buzz” generated by Pershing Square, @KidDynamite, Bronte Capital, and whoever else is calling $HLF a pyramid scheme.

median(Vo(HLF)) tells me the halfway split between “high” and “low” trading volume for this stock. It’s roughly 2 million trades per day. Then with quantmod I can plot those hi-lo subsets with chartSeries(subset(HLF, Vo(HLF)<2e6)); chartSeries(subset(HLF, Vo(HLF)>2e6)) to get a visual on “calm days” versus “heavy days”. That’s something you can’t do with Google Charts.

Here’s calm (under 2 million trades/day)


upper half of heavy trading days (over 2 million/day)


and what I’ll call “pirate days” (over 10 million trades/day)—with plunderers swarming the stock, battling with swords between their teeth


wherein it’s visually clear that very heavy trading skewed blue over gold—i.e. $HLF closed lower than it opened on that day: the heavy trading volume was taking the price downward.

But more precisely what happened on those days? This is a good job for the stem-and-leaf plot. Notice, by the way, that reality here looks nothing like a bell curve. Sorry, pet peeve. Anyway here is the stem plot of heavy trading days:

> hi.volume <- subset(HLF, Vo(HLF)>1e7)
> stem(Cl(hi.volume)-Op(hi.volume))

  The decimal point is at the |

  -14 | 1
  -12 | 
  -10 | 
   -8 | 2
   -6 | 1554
   -4 | 542
   -2 | 430
   -0 | 988761851
    0 | 345667780388
    2 | 058699
    4 | 1
    6 | 5

I love stem plots because they give you precision and the general picture at once. From the way the ink lies you get the same pic as the kernel density plot( density( Cl(hi.volume) - Op(hi.volume) ), col="#333333" , ylab="", main="Volume at least 10 million $HLF", yaxt="n", xlab="Price Movement over the Trading Day"); polygon( density( Cl(hi.volume) - Op(hi.volume) ), col="#333333", border="#333333" )
but you can also see actual numbers in the stem plot. For example the ones to the right of +0 are pretty interesting. Slight gains on many of those pirate days, but not enough to bash back a 14-point loss on a single day.

I’m bored of #ff meaning follow Fridays. Let’s do Failure Friday instead and talk about things we’e failed at.

  • I failed an arithmetic test.
  • I failed judo class.
  • I failed to attract interest with my CV.
  • I failed to be married or have a stable job by my 30th birthday.
  • I failed an entrance exam.
  • I failed most of my writing assignments.
  • I lost an important contest.
  • I lost a race. Badly.
  • I lost a client I thought I had secured.
  • I failed a client I thought I could help.
  • I failed to get paid what I thought I was worth.
  • I failed to be honest in a romantic relationship.
  • I failed to do anything cool for a few years.
  • I couldn’t walk on a mountain because I was so out of shape.
  • I failed to wear sunscreen.
  • I failed to read the prospectus.
  • I failed to get into my preferred university.
  • I failed to get someone to fall for me.
  • I didn’t know what I wanted or how to get it.
  • I failed to keep in touch with old friends.
  • I failed to impress people.
  • I failed to advocate for myself.
  • I failed to do things on time.
  • I lost Other People’s Money.
  • I failed to come up with good ideas.
  • I failed to give it my all.
  • I failed to lose weight.
  • I failed to meet expectations.
  • I failed to look “put together”.
  • I failed to stay organised.
  • I failed to Get Things Done.
  • I failed to cook a good dinner.
  • I failed to recognise the obvious signs.
  • I failed to learn what I was trying to learn.
  • Things did not go according to plan.

NB: I don’t intend Failure Friday as a pity party. It just bugs me when people try to act flawless and successful. Infinitely wise with inerrant self-command. Even apparent failures are successes in disguise. Sorry stories modulate into major key as the lessons learned were invaluable rungs on the ladder of upward progress so in the end it all worked out for the best.

What is that? You’ll probably just make people who are already down feel worse by doing that. And not make anyone feel better.

Evolutionary psychology … popular … media … people latch on to these stories and use them to justify the status quo. One … is that men prefer women with small waists and big hips. This is measured using the Waist to Hip Ratio (WHR). The WHR is the circumference of your waist divided by the circumference of your hips. The links below will tell you that men are irresistibly drawn to women with WHRs of .70. This number is apparently imbued with evolutionary significance because prepubescent girls have WHRs close to 1 (their waists are the same size as their hips), while post-pubescent girls have WHR less than 1 (waists smaller than hips); and also because low WHRs are associated with a good hormonal balance. One thing that makes this idea attractive is that it conforms to our modern, western experience—many women who are considered to be extremely attractive have low WHRs and it’s difficult to generate examples of women who are famous for their beauty, but who have high WHRs. …

… here are a couple recent stories about the WHR: 1 (this one includes exercise tips to help women appear to have a more ideal WHR ratio) 2, and 3 (this one also claims that “men’s perfect lovers come with a waist-to-hip ratio of .70”, implying, I suppose that WHR ratio influences how good you are in bed??). Science reporting is rarely subtle and these articles are no exception. They talk about “males”, “females”, “mate preference”, and “evolutionary” indicators of fertility. This language suggests to the average reader that these results are universal. That they reflect the preferences of people in general. But, does the research behind the headlines support this universality?

The burden of any serious evolutionary psychology research program must be to establish the generality of their results across cultures. It doesn’t matter how cool the evolutionary angle is— oh, look, this co-varies with fertility!!. It doesn’t matter how obvious the effect seems to us. If male preference for women with low WHRs doesn’t obtain across cultures then it’s not universal. This isn’t to say that there couldn’t still be an evolutionary component to our preferences. It would be remarkable if there were not. But, genetic contributions to behaviour are complicated. So, failure to establish the generality of a preference for low WHR doesn’t necessarily imply that men aren’t sensitive to information that conveys fertility in potential partners. But, it does mean that there is not a universal reliance on this one particular type of information. It is quite likely that a whole lot of cues interact in a complex system of perceived attractiveness, to the extent that it doesn’t make much sense to isolate one variable. So, anyway…

What IS the evidence for a low WHR … preference across cultures? Well, it’s actually quite muddled. Westman and Marlowe (1999) provide a pretty good intro to the evidence for the WHR preference, so I’d recommend their paper for a quick overview. They point out that the majority of studies on WHR rely on American undergraduates, although there is also evidence for a similar preference in Hispanic, British (although see below), and American-Indonesians. Some researchers (e.g., Singh, 1993) suggest that this preference is universal across cultures (p. 305). But, rather than jump straight into a statement of universality, Singh says something a bit more measured. He claims “the fact that WHR conveys such significant information about the mate value of a woman suggests that men in all societies should favor women with a lower WHR over women with a higher WHR for mate selection or at least find such women sexually attractive.” That last bit is interesting. It merely suggests that men shouldn’t find women with low WHR unattractive. This is a very different argument than the oft repeated universal preference for low WHR.

Unfortunately, Singh’s … prediction has morphed into a presumption of universal preference for low WHR. ….
But, as it happens, there is quite a bit of evidence against this claim. Westman & Marlowe (1999) tested the effect of weight and WHR on perceived attractiveness, health, and suitability as a wife in the Hadza of Tansania. The men in that society showed no preference for women with low (.7) or high (.9) WHR, but they did show a distinct preference for heavier (cf. thin) women. Yu and Shepard (1998) also failed to find an effect of WHR on attractiveness among the Matsigenka. Swami et al (2007) looked at WHR preferences among males in Spain, Portugal, and the UK.In all three countries BMI, not WHR, accounted for the most variance in perceived attractiveness. WHR influenced attractiveness judgments for Spanish and Portugese, but not British men. However, even in the Spanish and Portugese samples WHR accounted for only about 18-19% of the variance, while BMI accounted for over 70% of the variance in perceived attractiveness. This paper also has a great summary of methodological issues with prior WHR studies (e.g., the use of two dimensional line drawing, failing to control for BMI). Cornelissen et al (2009) looked at patterns of British male gaze fixation during attractiveness judgments of pictures of women. Men tended to look at the upper abdomen and face, not the hip or pelvic area. The pattern of gaze fixations matched the way men evaluated the same pictures when estimating body fat, and did not match the way men evaluated WHR. Reading these papers suggests a lively debate in the literature about the universality of low WHR preference. I am not an expert in this area, and these examples don’t even scratch the surface, but they do indicate lack of consensus on the generality of the low WHR preference.

So, what does WHR even mean, evolutionarily speaking? Most people seem to argue that low WHR indicates a good balance of estrogen to other hormones, which is important for fertility. Fertility, undoubtedly, is essential to evolutionary fitness but 1) WHR isn’t going to be the only cue to fertility and 2) there are other important characteristics that may account for more variance in reproductive success in some situations (e.g., if the vast majority of women in a certain age range are fertile). Cashdan (2008) looked at actual average WHRs in a variety of cultures, mostly non-Western. She found that the average WHR was > .80 (remember, .70 is supposedly the magic number). Cashdan pointed out that androgens and cortisol both increase abdominal fat in women (increasing WHR). But, higher levels of these hormones are also associated with increased strength and stamina, which come in handy in less than optimal circumstances. She says: “Waist-to-hip ratio may indeed be a useful signal to men, then, but whether men prefer a WHR associated with lower or higher androgen/estrogen ratios (or value them equally) should depend on the degree to which they want their mates to be strong, tough, economically successful, and politically competitive” (p. 1104). This suggests that it’s possible to construct a perfectly reasonable evolutionary account for why men might prefer a high, rather than low, WHR (i.e., given a stressful environment where strength and stamina matter). The variables that dominate in a particular situation will likely depend on a number of specific environmental and cultural conditions. In other words, it’s complicated.

This story, unlike the one about low WHR preference, doesn’t seem to reflect our (modern, western) experience, so it’s less likely to catch the popular imagination. We don’t tend to think of male attraction based on female heartiness, but we also live in a particularly rich culture where we don’t spend a lot of time physically searching for / killing food or building shelters. So, here’s the psychologist’s fallacy again. Evolution is complicated and the features that confer fitness are necessarily dependent on context. This means that it’s not too difficult to think of a number of plausible evolutionary explanations for a particular phenomena. The preferred explanations are most likely going to be the ones that fit with our current experience, but this doesn’t make them better explanations.

via until a single soliton survives

supervenes and I were discussing our New Year’s resolutions. He said he partitions people into those who:

  1. already work on their goals without NYE resolution or "wipe the slate clean" dreams — they don’t need a New Year to follow through on their goals
  2. set goals they won’t actually follow through on (like a perpetual weight-loss goal).

I feel I fall into a third category, which is not knowing what would be worth setting a goal to achieve.

I do think that

  • setting achievable goals,
  • organising your time so you make sure you do the things you want to (including have fun),
  • personal accountability,
  • writing down long-term aspirations,
  • balancing the short-term versus the long-term 1 2 3,
  • nudging yourself,
  • cutting out crap,
  • and so on

can be really effective. I’ve done those things before and I’ve felt the thrill of looking over past to-do lists and thinking “Yeah, I really did that! Score one for me!”

I tried to learn to do a flip last summer. Didn’t get there. I guess I will try again this summer and try to remember to do stretchy back-bends in the winter to prepare. It would be nice to be fast again, or faster than I ever have been. I’m not sure if I care enough to really do what it takes to be fast. I’d like to learn a lot of things. I’m not sure that’s really worth committing to either. I know if I really made it a priority, cut out  I could accomplish ≥1 of those.

But what’s really worth doing? What would make me truly, deeply happy and what’s my Engel curve on the way to there? Who would I become if I actually achieved my goals and do I want to “feed” or be that person? Those questions are beyond my ken.

Everyone around me is full of robust certitude. I alone am tentative. Lao Tzu

I couldn’t even answer simple ones, like:

  • Should I buy an iPad? They look cool so in a sense I “want” one. But from observing how I react to other computing opportunities (addictively), I think buying it would lessen my self-control and I would just end up reading a bunch more news instead of doing what I want to.
  • Should I read Steve Awodey’s book? It looks good and I could find a copy in a nearby university library. But are those 300 pages worth all the other time I would have to give up? (And how much time might that be?)
  • You never know until you try. Let’s take a longer-term goal to add even more uncertainty. "Grad school is a gamble"—if I set the goal to get a Ph.D., I barely know what I am in for, much less what I will turn into at the end, what kinds of new opportunities it would present for me, whether I would use it later, or whether that sum-total package is "worth it" to me or not. Without knowing what will result how could I say whether I want it or not?
  • Will I keep blogging? It has been a huge timesink. However it’s also very flattering to have so many people listening to my various opinions. It has felt good to get certain things out of my brain-soup. And I have met some cool people here and on twitter. Still it is a huge timecost. I’m not really sure where the balance lies. So for lack of a confident reason to choose one thing or the other, and with my natural risk-seeking personality wondering what “might” happen at 100,000 followers or what “might” happen if I finish writing down all the ideas I wanted to since before I decided to try writing—inertia will probably win and I will continue as is.

If I decided some concrete, measurable, not-too-large goal like “Run 300 miles over the year”, it could be easy to achieve. But start from a real goal like: “I want to be happier”. Well what will make me happier? I don’t know, it could be a raft of things. A complex of big & small and once-off & daily changes. I could try some and maybe they wouldn’t or would work. Maybe I would be able to tell and maybe not. Maybe I should continue what I’m doing and it will just take longer to work.

"never give up" -- nonmonotonic returns

So I’m going into 2013 without any measurable goals or definitive plans to achieve them. Yet hoping 2013 isn’t a repeat of 2012.

  • chimp and neandertal musculature versus modern human musculature
  • and the Wadabi tribesmen dress up in women’s garb and makeup, and starve themselves, before performing beauty dances to please the women. They expend enormous efforts (time and calories) obtaining cosmetics; favourite gift is a mirror.
  • Akaa pygmies reproducing with everyone and the typical evo psych concept of sexual jealousy seems to not apply.
  • Akaa fathers do more of the parenting than the mums.
  • 1:07 — Bedouin Wa-alla sons speak to their fathers ~3 times up to age 18, and transgressions punished by stabbing with a sabre.

Amazing New Easy Way Puts on Pounds and Inches
Firm, Solid Flesh without overeating

Life Magazine Ad. 1964


Amazing New Easy Way Puts on Pounds and Inches

Firm, Solid Flesh without overeating

Life Magazine Ad. 1964

(Source: httpcolonslashslash)


Chen […] thinks that if your language has clear grammatical future tense marking […], then you and your fellow native speakers have a dramatically increased likelihood of exhibiting high rates of obesity, smoking, drinking, debt, and poor pension provision.

And conversely, if your language uses present-tense forms to express future time reference […], you and your fellow speakers are strikingly more likely to have good financial planning for retirement and sensible health habits.

It is as if grammatical marking of the difference between the present and the future insulates you from seeing that the two are coterminous so you should plan ahead. Using present-tense forms for future time reference, on the other hand, encourages you to see that the future is just more of the present, and thus encourages you to put money in a 401(k).

Geoff Pullum


I googled Emma Watson's weight, because someone was saying that she is only considered attractive because she's skinny. The #2 Google result claims that her weight is between 110-112; the #1 and #3 Google results claim she should lose weight.
F___ that. WolframAlpha puts her exactly on the line between normal and underweight by BMI. (But if the community got data from when she was under 18 years old, a lower BMI can be normal).

I googled Emma Watson's weight, because someone was saying that she is only considered attractive because she's skinny. The #2 Google result claims that her weight is between 110-112; the #1 and #3 Google results claim she should lose weight.

F___ that. WolframAlpha puts her exactly on the line between normal and underweight by BMI. (But if the community got data from when she was under 18 years old, a lower BMI can be normal).

People over-estimate what can be done in the short term, but under-estimate what can be done in the long term.

Weight loss is a perfect example. People think they can get in shape in a couple weeks before going to the beach (hard to do). But they don’t realize how much weight they could lose over a year by eating just 100 calories under their daily metabolic usage (answer: 10 pounds per year, i.e. 100 pounds per decade! from 1 tweak).

Futurists also over-estimate the magnitude of short term changes and under-estimate the long term magnitude. I remember there were so many prognosticators in the 90’s, during the Internet boom — it was all New Economy this and paradigm shift that.

So now that everybody is (ahem, Americans are) logging into Facebook on their iPads — who was right and how? I mean, who are some futurists who said we would be in roughly the place we are now — and what did they think of “us”, the “future people”?