Abstract
Despite the vast public policy efforts to promote the consumption of healthy foods and the public’s growing concern with weight management, the proportion of overweight individuals continues to increase. An important factor contributing to this obesity trend is the misguided belief about the relationship between a meal’s healthiness and its impact on weight gain, whereby people erroneously believe that eating healthy foods in addition to unhealthy ones can decrease a meal’s calorie count. This research documents this misperception, showing that it is stronger among individuals most concerned with managing their weight—a striking result given that these individuals are more motivated to monitor their calorie intake. This finding has important public policy implications, suggesting that in addition to encouraging the adoption of a healthier lifestyle among overweight individuals, promoting the consumption of healthy foods might end up facilitating calorie overconsumption, leading to weight gain rather than weight loss.

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Background

In introducing today’s paper, I am reminded of an old joke/quip to the effect that “All that separates man from the animals is our ability to rationalize.”   I’d add “And accessorize” but that’s neither here nor there.  But the reality is that humans are able to do a wide variety of mental gymnastics in how they approach life.  Effectively, we appear to be slave to what psychologists call cognitive biases, ways in which we think about the present, past, future or ourselves that often lead us to make some fascinatingly bad choices.  This is a topic that many recent books has discussed in a variety of contexts.

And while I don’t know if I can say that it occurs to a greater degree in terms of eating and health behaviors, there is no doubt that people often engage in some exceedingly interesting mental gymnastics when it comes to those topics.  Some of this is conscious but much of it can be chalked up to either unconscious behaviors, misunderstandings (or a lack of information/education) or mishearing/misinterpreting the message.   And these types of things, as much as anything else, often derail many people’s attempts to eat healthy, lose weight or simply avoid weight gain.

In the realm of exercise for example, many people grossly overestimate the actual caloric expenditure from activity, as I discussed in Normal Weight Men and Women Overestimate Energy Expenditure – Research Review, and this leads them to either expect far more of an impact on weight loss than is realistic or to eat more calories than they actually need based on the assumption that they burned it off during activity.

In the arena of eating, this issue can show up in a myriad ways.  A classic example of a misunderstanding/garbling of the message occurred back in the 80′s during the low-fat eating craze.   While it’s hard to say where the blame lies, the general public sort of got the message that so long as they kept fat intake low, nothing else really mattered.  Caloric intake and portions went out the window. 

Food companies capitalized on this by rushing plenty of energy dense, high-calorie (but low-fat) foods to market and it all went wrong.  Studies routinely found that people ate more food when it was labelled ‘low-fat’ compared to one that was labelled as being higher in fat.  Either consciously or unconsciously, they gave themselves permission to eat more of it.  And often ended up consuming more calories than they would have otherwise.

Another example deals with artificial sweeteners where you often see a pattern where artificial sweetener (or diet soda) intake is associated with weight gain (or a lack of weight loss).  And while there is some speculation that artificial sweeteners do some odd things in the brain in terms of driving appetite, it’s probably more related to people rationalizing that they can eat more of something else because they are getting less calories by choosing diet soda or using artificial sweeteners.   That is, they figure that since they are ‘saving so many calories’ by making one choice, they end up compensating (or more than compensating) by choosing something unhealthy.  Call this the skim milk and chocolate cake or Diet Coke and cheeseburger approach to eating. 

I’d note before continuing that this much of the above rationalizing tends to be more for people who are only paying somewhat ‘superficial’ attention to ‘eating well’ (or some other fairly abstract goal).  That is, the type of thing I’m going to talk about doesn’t generally occur among folks who are diet obsessed and track macros or calories or what have you.  Rather it’s for folks who, while they may say that they are concerned with their diet or body weight or body fat, are focusing on the wrong things (a topic I addressed in more detail in Fundamental Principles vs. Minor Details).

Finally type of behavior seems to occur more prevalently in people who tend to divide foods into ‘good’ and ‘bad’ categories (a category that many popular diets and dietary approaches tend to promote).  ‘Good’ foods become equated with healthy and, altogether too often, can be eaten without consequence (i.e. weight gain).  Researchers call this the ‘health halo’ by which supposed ‘healthy foods’ have a halo of invincibility around them  In the same vein ‘bad’ foods are equated with being unhealthy and this categories are not only absolute but cause us to do some of those strange mental gymnastics when it comes to how we approach our food intake.

You can find examples of this all over the place where people assume that ‘healthy/good’ foods can be eaten in uncontrolled amounts whereas the tiniest amount of ‘unhealthy/bad foods’ mean that the diet has failed, the dieter is immoral and weak, and health will simply be destroyed (this is seen at the greatest extreme in a psychological condition called orthorexia whereby people see food as a moral choice judging not only themselves but others by the foods that they choose to eat).  You can see some good examples of this in the comments section of Straight Talk About High-Fructose Corn Syrup: What it is and What it Ain’t. – Research Review. 

Which basically segues into today’s paper which examines a behavior pattern that is often seen whereby folks tend to get fixated (or perhaps ‘blinded’ is a better word) by the concept of ‘healthy’ foods and end up missing the forest for the trees when it comes to their food and caloric intake.   There is also evidence that people who are (or at least state that they are) more ‘weight conscious’ are even more prone to make these kinds of mis-estimations which was a secondary aim of the study.

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The Paper

The study recruited 934 people, of whom the majority (74.2%) were female aged anywhere from under 20 to over 50.  Subjects were then shown 4 meals which either consisted of ‘unhealthy’ foods or those same unhealthy foods coupled with a healthy option.  The four meals, with the healthy addition shown in parentheses, were a hamburger (three celery sticks), bacon and cheese waffle sandwich (small organic apple), chili with beef (small salad without dressing) and meatball pepperoni cheesesteak (celery/carrot side dish).   So, for example, subjects were either shown a bacon and cheese waffle sandwich (which sounds amazing in so many ways) either by itself or side by side with a small organic apple.

Half the subjects were shown the unhealthy choice alone and the other half were shown the combination of the unhealthy choice with it’s healthy add-on and they were asked to estimate the caloric value of the meals.  I’d mention that this design is problematic because it’s not comparing how a given individual might rank each of the two meals; rather it’s comparing the average estimate of the caloric value of the different meals between people.  All subjects were also asked to rate how concerned they were with managing their weight on a scale of 1-5 (with 5 being extremely concerned).

The study generated a total of 2750 total observations of the different meals and, on average, subjects estimated that the unhealthy meal alone contained 691 calories.  Now, logically it’s obvious that a food consisting of an unhealthy item PLUS a healthy item would have to have more calories than the unhealthy item alone.   Clearly two foods can’t have less calories than either food alone.

Yet, on average, subjects estimated the unhealthy plus healthy choice as having only 648 calories.  I’d mention that as a third part of the study, a separate group was asked if they believed that the healthy foods contained negative calories and this was not the case.  So it doesn’t appear to have been the case where subjects figured that the healthy addition was literally ‘reducing’ the caloric value of the food by containing negative calories.  Rather, the ‘health halo’ effect caused people to systematically underestimate the caloric value of the combination of an unhealthy and healthy food.

But it gets even odder.  When the estimates were ranked by how folks reported their concern with managing their weight, the values changed even more.  The most ‘weight conscious’ subjects estimated the unhealthy meal as containing 711 calories while the combination of the unhealthy and healthy choice was only 615 calories. In contrast, the non-weight conscious individuals estimates were only 684 for the unhealthy choice versus 658 for the combination and there was a direct relationship between how weight conscious the subjects were and their mis-estimate of the different meals.

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My Comments

I really don’t have a ton to add to the above, the paper goes into lot of discussion that I’ll spare you here since it’s a lot of detailed examination of the possible underlying mechanisms behind these types of odd cognitive biases.   One point that was made was that while one might expect more motivated/involved people to have less problems with these types of conceptual biases, this research found the opposite.  To whit:

The negative calorie bias is more pronounced for more involved/motivated individuals. Thus when evaluating vice/virtue combinations, greater motivation does not necessarily result in greater accuracy but instead can lead to more biased judgments.

I would add that I think really has more to do with what I mentioned in the background above, the issue isn’t with dietary motivation per se but rather with how people often conceptualize the process.  By focusing on things like good/bad foods, clean vs. unclean eating, meal frequency exclusively or organic vs. non, people lose sight of the issue of portions and calories which are what really matter when it comes down to it. They rely on estimates which are oh so often off.  And which appear to be colored heavily by the cognitive biases that many humans are so prone towards.

Make no mistake, certain types of eating patterns often automatically get people to reduce their intake, often by the outright removal of a so-called ‘bad’ food.  What is defined as good or bad depends on the diet in question and certainly these types of good/bad approaches to dieting can work in at least the short-term (and sometimes longer than that).  The problem is when people start focusing on the goodness/badness of the foods they are eating to the exclusion of everything else.  That’s when it often goes wrong; this is not helped by many dietary approaches telling folks that calories/portions don’t count and that focusing only on the aforementioned ‘good/healthy’ foods is all that matters.

In this vein, the paper’s author notes that:

In particular, the negative calorie illusion has been shown to be less pronounced when individuals pay attention to the quantity of the combined items, instead of focusing solely on the healthy/unhealthy aspects of the items.

In a related vein, the author points out that:

Another public issue raised by this research concerns the viability of promoting the very notion of stereotyping foods into vices and virtues.  Despite it’s intuitive appeal as a decision heuristic to simplify choice, vice/virtue categorizations focuses consumers’ attention only on one aspect of the meal [my note: whether the food is a 'vice' or a 'virtue'] and ignores other important aspects such as its overall quantity.

And I really think that that’s the big take home message of this rather odd paper: people often get so fixated and focused on the wrong things that they end up hamstringing their own attempts to reach their goals.  Because while it’s all well and good to focus on healthy/unhealthy, good/bad, clean/unclean or whatever, at the end of the day quantities always count.  When people lose sight of that and focus on the wrong aspects exclusively, they often end up hurting their own progress.  This paper just points out one way that this happens. 

I’ll finish by pointing interested readers to a book by the paper’s author titled  The Dieter’s Paradox: Why Dieting Makes Us Fat that addresses not only this research but a great deal of other research looking at similar issues.  How humans tend to categorize foods into good and bad and how it can lead them to make a lot of really weird assumptions about what they are actually eating.  It was a pretty fascinating read and shows how many different ways we can end up screwing our own progress by relying on our (often incorrect) intuition, primarily by focusing on the wrong factors that are relevant to what we are eating.

Objective: To test whether physiological satiation as measured by the gut peptide ghrelin may vary depending on the mindset in which one approaches consumption of food. Methods: On 2 separate occasions, participants (n = 46) consumed a 380-calorie milkshake under the pretense that it was either a 620-calorie “indulgent” shake or a 140-calorie “sensible” shake. Ghrelin was measured via intravenous blood samples at 3 time points: baseline (20 min), anticipatory (60 min), and postconsumption (90 min). During the first interval (between 20 and 60 min) participants were asked to view and rate the (misleading) label of the shake. During the second interval (between 60 and 90 min) participants were asked to drink and rate the milkshake. Results: The mindset of indulgence produced a dramatically steeper decline in ghrelin after consuming the shake, whereas the mindset of sensibility produced a relatively flat ghrelin response. Participants’ satiety was consistent with what they believed they were consuming rather than the actual nutritional value of what they consumed. Conclusions: The effect of food consumption on ghrelin may be psychologically mediated, and mindset meaningfully affects physiological responses to food. (PsycINFO Database Record (c) 2011 APA, all rights reserved).

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Background

Ok, in addition to having possibly the coolest title of any paper I’ve reviewed on the site, this is also one of the weirdest papers I’ve looked at.  But I’ve seen it getting a lot of press and, of course, have to put in my own two cents, if for no other reason than I suspect many people will take the findings far out of context.  First, some necessary background; this will probably take more space than discussing the actual paper itself.

In recent years, the number of factors in the body controlling hunger has multiplied by leaps and bounds.  Leptin was discovered in 1994 or so and since then numerous other compounds have been identified that play some role in hunger, appetite, body weight regulation or even body composition.  One of those is ghrelin, which I also talked about in more detail in the Bodyweight Regulation Series.

In brief, ghrelin is a compound released from the gut in response to a whole host of physiological factors.  Among other things, ghrelin binds to a receptor in the brain and stimulates growth hormone release (useless trivia for the day: ghrelin was a compound where the brain receptor was discovered before the hormone itself was discovered; it was actually the presence of the receptor that drove researchers to go look for what was supposed to bind to it).

More relevant to today’s article, increases in ghrelin stimulate hunger (and alter fuel utilization and calorie partitioning, at least in animal models) and acute injections of ghrelin reliably increase hunger.  As well, ghrelin antagonists reliably blunt hunger.  Please note that ghrelin is one of those hormones where, in a sense, high levels are ‘bad’ and low levels are ‘good’, at least from the standpoint of things like hunger and appetite.  As is to be expected, as leptin levels fall on a diet, ghrelin typically goes up.

One of the oddities of ghrelin was that levels appeared to change in anticipation of meal time.  That is, through some mechanism (that so far as I could tell was never determined), ghrelin levels would go up just prior to normal meal times.  So if you habitually ate lunch at 12pm, ghrelin would go up right before then, lowering blood sugar and making you hungry.   Scientists call this entrainment and ghrelin levels would entrain to normal meal times through some mechanism or another.

Tangentially, this probably explains why changing meal frequency is at least initially difficult: ghrelin levels are changing in accord with your normal meal times and get out of synch with the new one.  So if you’re trying to increase meal frequency, you initially find that you’re simply not hungry when you’re supposed to eat.

And if you’re trying to decrease meal frequency, for a few days at least, you’re ravenous at the times you used to normally eat, at least initially.  However, over a few days time, ghrelin entrains to the new meal frequency and you stop being hungry when you used to eat, only getting hungry when you’re habitually eating.

Most of the early studies on ghrelin looked at how food intake, calorie intake and macronutrient intake and such were impacting on ghrelin levels.  Carbs seemed to have a greater impact than fat, protein was unclear (at least the last time I looked at the research); mind you ghrelin is not the only hormone of relevance.  I look at a bunch of the others in Bodyweight Regulation Wrap-Up: Other Hormones.

The total caloric value of the meal seemed to play a determining role; more calories dropped ghrelin more than fewer calories.   I seem to recall one odd study where sham feeding (I think they gave them noncaloric fiber or something) reduced ghrelin which is the first indication that something weird was going on: how could thinking you were eating something lower ghrelin?  Ok, that’s the first half of the background on this paper.

The bottom line is that the above is all good and well and interesting and relevant.  And would be the final word in all of this if humans were nothing more than a gut and a nervous system. That is, if we just responded in a lovely deterministic way to changes in hormones, this would all be a lot simpler.  Sadly, that’s not the case.

Big brained humans have this thing called self-awareness, sometimes we even use it for our own benefits.  We can think, reason, etc. and this impacts on many things including hunger, appetite, food choices, etc.  So while most animals will pretty much eat when hungry and not when full, it’s not nearly so clear cut in the case of humans.

Humans will eat out of boredom, depression, because they are at a party.  Most eat more on the weekends and there is a reliable relationship between the number of people at a meal and how much people eat: more people and folks eat more food.  My point is that you can’t just look at the physiology of what’s going on and ignore the psychology or other aspects.

A simple example is that of anorexia, or even dieting in a more general sense: in the case of full blown anorexia there is a situation where despite presumably massive drive to eat, the individual consciously chooses not to do so (I’d note here a brand new study, that I am still trying to get ahold of where researchers are suggesting a metabolic ‘brokenness’ contributing to anorexia and driving the psychology; I suspect it’s a complex loop where one is driving the other).

Even dieters, in the face of hunger, make conscious choices whether or not to ‘obey’ the signals being sent by hormones.  Basically, humans do not represent some deterministic system where you just look at the hormones and go ‘This is what’s going to happen’.  And if that didn’t complicate things enough, it’s clear that people differ in their psychological approach to things like eating.

Researchers often talk about things like restrained and unrestrained eaters, rigid versus flexible dieters (a topic I looked at in my own A Guide to Flexible Dieting), disinhibited eaters and others and there are clearly different psychologies when it comes to how people approach eating, food restriction, overeating.

And it won’t be surprising to find that all of the above psychological  (along with physiological) stuff differs to at least some degree for lean versus obese individuals.  We already know that there can be an insensitivity to leptin in the brains of the obese (whether this is a cause or effect of obesity is still up to debate) and there is evidence of differing sensitivity to other hormones such as ghrelin, GLP-1, PPY and the rest of the mix.

Basically, human hunger and appetite and real-world food intake is very complicated and something the strangest things impact on food intake in a way that you might not necessarily predict ahead of time (i.e. who would have thought that having more people present at a meal would lead to higher food intakes).  One of those is related to belief or how people can often rationalize certain food choices because of the situation or what else they are eating.

Here’s a classic example that will finally segue into today’s paper: back during the low-fat craze someone did a study more or less along the following lines.  Folks were given a food (I think it was frozen yogurt) and then ‘told’ that it was either low-fat or full/high-fat yogurt.  Note that the yogurt was identical in both cases, they were simply told that they were being given different types.  Subjects who thought they were eating the low-fat yogurt ate more.  Presumably they rationalized that since it had less calories/fat, they could eat more of it and this, among many other factors, has been held up as one reason that the whole low-fat movement failed.

You might also look at this as the Oreo/skim milk or double cheeseburger/diet coke effect whereby people rationalize eating something crappy because they are ‘balancing’ it out with something healthy or whatever.  This is often given as a reason that things like diet sodas fail to impact on bodyweight; some people simply justify eating more of the other stuff because they aren’t getting calories from the diet soda.  Yes, there’s more to it than that but this introduction is already way too long.

My point is this: human appetite and hunger is clearly an interaction between physiology and psychology, not that you can ever really separate the two (as I discussed in Dieting Psychology vs. Physiology before I lost the plot of what I was trying to talk about).  Because physiology impacts on psychological function.  And, as today’s study shows, psychological function impacts on the physiology of eating behavior. Ok, on to the paper.

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The Paper

46 participants, between the ages of 18-35, within a normal to overweight BMI were recruited to take part in two separate sessions for the study.  Of those 46 people, 65% of the subjects were women, 56% were white, 12% African American, 11% Asian American, 10% Hispanic/Latino and 11% other.  Subjects were told that the Yale nutritional center was working on two different milkshake with differing nutrient contents that they would sample and that the goal of the study was to evaluate whether or not the shakes tasted different and to examine the body’s reaction to the different shakes.

Basically, they were lied to; the milkshakes were identical in composition, but were presented with two different labels, which I’ve shown below.

Decadence You Deserve

Guilt Free Satisfaction

So the indulgent shake was presented as a high fat, high calorie shake and the sensible shake was touted as being low fat and low calorie.  Again, the shakes were actually identical in terms of their nutrient and caloric content, all that differed was the labelling.   Each day lasted 2.5 hours divided into two time intervals.  In the first interval, after 20 minute rest period, blood was drawn at 60 and 90 minutes and the subjects were asked to rate the label of the shake in terms of hunger ratings.

In the second interval, subjects drank the shake within 10 minutes and were asked again to rate hunger along with taste (including smell and taste along with enjoyment and healthiness).  If you’re wondering how hunger is rated, it’s done subjectively through something called a Visual Analog Scale (VAS), a little graphic doodad that ranks things from 0-100; it’s subjective as hell but used commonly.

Measurements of ghrelin were made from the blood draws and subjects also filled out a questionnaire to determine their degree of dietary restraint and this was used to see if there was any effect of dietary restraint on the other variables measured.

So what about the results.  Not shockingly, subjects rated the sensible shake as 7 times healthier than the indulgent shake and the degree of restraint had no impact on this.  Basically, they firmly believed that the sensible shake was healthier.  No differences were seen in the ratings of tastiness between shake conditions.

Ok, let’s look at ghrelin first since this seems to be where most of the Internet punditry is focusing.  The indulgent group showed a much higher rise in ghrelin prior to consumption of the shake followed by an equally significant drop after consumption.  In contrast, the sensible shake situation found a fairly flat ghrelin change: there was a small increase with little change downwards.  I’ve shown the actual changes in ghrelin in the graph below and you can see the clear difference in ghrelin response pattern for the two conditions.

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Changes in Ghrelin

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Looking solely at this physiological response, the researchers state:

When drinking the shake in an indulgent mindset, participants’ levels of ghrelin reflected a moderate level of physiological craving followed by a significant level of physiological satiety…when drinking the shake in a sensible mindset, suggesting that, despite consuming the same nutrient contents, they were not physiologically satisfied.

So good intersting stuff, mindset affects physiology and the folks who thought they were indulging had a significantly different ghrelin response, suggesting of differences in both craving and satiety, than those who thought they were eating the sensible shake.  Clearly how you approach your diet meals or whatever can impact your physiology and all you have to do is adjust your mindset to sail along in your diet.  Right?

But, now, you’re wondering, what’s the catch in all of this because we all know I wouldn’t be discussing this paper if there wasn’t something more going on?  Be patient, I know this is long but bear with me.

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My Comments

Today’s paper is certainly interesting, if nothing else it indicates that an individual’s mindset going into eating something can affect at least one marker of physiology, in this case ghrelin.  Specifically, the ghrelin response to the shake intake was based on the person’s expectancies of the shake rather than it’s actual caloric content.  And that’s what the researchers talked about in the abstract: how mindset affected this one singular physiological response.

That’s most of what I’ve seen people focusing on in their commentary about this study on the Internets (one title was “It’s All About the Hormones”).  It’s also why I spent so much time in the introduction trying to point out that humans aren’t just physiological automatons responding to changing hormones in this fashion.  Because it’s not just about the hormones in humans.  There’s way more going on.

Before moving on the researchers point out something that most seem intent on ignoring: this was a single meal study.  As the researchers state:

Although the effects of such psychologically mediated differences in subsequent consumption or long-term alterations in weight were not measured in this particular study, future research on the impact of this phenomenon on metabolic maintenance is warranted.

I don’t disagree and in the real-world the above matters since what is seen acutely often doesn’t translate at all to the long-term; you often see compensation at the next meal or the next day or whatever and it all balances out in the wash.  Simply, few conclusions can be drawn from this one study in terms of food intake across a day, a week, a month.

Don’t misread me: I’m not saying it won’t or couldn’t have an impact.  It might or it might not.  But it might also all balance out given that there are other systems regulating things as well.  There is also the fact that, as I discussed in Homeostatic and Non-Homeostatic Pathways Involved in the Control of Food Intake and Energy Balance the physiological systems present in humans can clearly and easily be overwhelmed by non-physiological factors (such as how many people you are eating with).

Looking at their results, the researchers get into a whole speculative discussion about how some of the mindset of dieter’s about their food might be contributing negatively to overall results.  For example, based on data that increased ghrelin tends to drive hunger and lower metabolic rate (at least in animal models), they speculate that:

The relatively flat ghrelin profiles in response to consuming the shake in a sensible mindset may be placing participants in a psychologically challenging state marked by increased appetite and decreased metabolism.

Two problems with this.  One they didn’t measure metabolic rate and speculating from what is mostly animal data is a mistake. But that’s not the bigger issue here, it’s time to focus on their claim of increased appetite (or perhaps less blunting of hunger).

Did you notice something missing in my discussion of the study above?  Like how I mentioned that they measured hunger using a Visual Analog Scale early on but then didn’t say anything more about it?  It’s because I was saving it for now. In a single throwaway sentence hidden at the end of the results section that nobody who has just read the abstract will actually see, the researchers state what I think is the truly important finding that everyone seems to be ignoring:

For the measure of hunger, these analyses produces no significant main or interaction effects as a function of shake, time or restrained eating.

Translated into English that means this: despite the changes in ghrelin as a physiological marker of craving and satiety there was no difference in hunger between the indulgent and sensible condition and dietary restraint had no impact on this.   So the differential ghrelin  response, while interesting, didn’t amount to anything in the real-world in terms of actual hunger ratings differences between the two mindsets.  Please read that sentence again until it sinks in.

Hell, the researchers didn’t even bother to provide the VAS hunger data for the different conditions anywhere in the paper.  They just went through this whole involved discussion on ghrelin and everything else and then, as an afterthought mentioned “Oh yeah, there was no difference in actual hunger.”  And they didn’t mention it in the abstract.

So while the physiological response they measured is nifty as hell and certainly worthy of more research, the simple fact is that it didn’t amount to any real world difference in actual hunger.   Which is important because people are already taking this paper completely out of context, going from the hormonal response (which was different based on mindset) and extrapolating that to differences in hunger (which was not different based on mindset).

A question might be why there was no difference in hunger and the best the researchers could do was to say:

This study did not find any significant differences with respect to subjective hunger regardless of mindset after participants consumed the milkshake.  This result may have been a function of the measuring timing (hunger levels were assessed 10 min prior to ghrelin changes as opposed to simultaneously or subsequently), or the manner in which hunger was measured (visual analog scale).

Basically they are crapping on their own measurement methodology to try to dismiss their non-result.   Don’t get me wrong, maybe they would have seen a difference in hunger had they measured things differently.  At least they used the word ‘may’ above.

I’d note that the VAS is used extensively to measure hunger and has been for years now; so far as I know, it’s pretty accurate and able to discriminate different levels of hunger.  So saying ‘The measurement method we choose might have sucked’ is kind of weak given that VAS is a commonly used measure.  And, of course there are other potential reasons that their nifty physiological response didn’t generate a real world change in hunger.

Maybe those small changes in ghrelin were irrelevant to overall hunger drive (the graph looks pretty impressive but the absolute difference in ghrelin wasn’t huge).  Maybe that kind of small change just isn’t enough to have an impact.  Maybe you need to factor in the myriad other hormones such as leptin, GLP-1, PPY and the rest when you look at this; there are too many overlapping systems here to just focus on a small difference in ghrelin response and then extrapolate to the entire system. Or maybe it was something else going on. We don’t really know until more research is done and speculating is kind of pointless at the end of the day.

What we do know is this: psychological mindset impacted on the response of a singular hormone that is important in terms of hunger drive and satiety.  But despite a measured physiological change in that hormone that differed between groups, there was no difference in real world hunger based on psychological mindset.  The hormonal response simply didn’t amount to anything in the real world.

And that last paragraph above is really my main point, and yes I took a long time to get to it.  Everywhere I’m seeing folks prattle about ‘thinking themselves thin’ hoping that thinking that what they are eating is more indulgent than it is while dieting will lower ghrelin and make them be less hungry.  But that’s absolutely not what the paper found as there was no difference in actual hunger ratings based on mindset.

As well, I think you could just as easily parse this paper to suggest that the indulgent group would be more likely to overconsume calories due to the early increase in ghrelin.  That is, if increasing ghrelin drives hunger, who’s to say that folks won’t eat more of a food if they put themself in an indulgent mindset? Especially if they aren’t in the artificial situation where regardless of hormonal response, they are being given a fixed calorie shake to drink.

Mind you, the study didn’t find this either since, beating the dead horse, hunger ratings didn’t differ at any time point for either group.  But it would be just as accurate an interpretation of the physiological response as assuming that the drop in ghrelin will decrease hunger (which it didn’t).

So here we have a fascinating paper, clearly things are more complex than we even thought up until this point, and this study shows that psychological mindset can impact on at least one measure of the physiology of hunger regulation (the mechanism wasn’t even guessed at).  Hopefully more work on this will determine not only if there is an impact but what the mechanism of it all is.

But at the end of the day, it didn’t amount to any actual change in real world hunger which is what matters.  More research is needed but drawing unwarranted conclusions from this paper is a mistake even if that’s what the Internet is doing right now.  So the physiological response while interesting as all hell simply had no real-world impact on actual hunger.  That’s the bottom line.

INTRODUCTION: The magnitude and duration of the elevation in resting energy expenditure following vigorous exercise have not been measured in a metabolic chamber. This study investigated the effects of inserting a 45-min vigorous cycling bout into the daily schedule versus a controlled resting day on 24-h energy expenditure in a metabolic chamber.
METHODS: Ten male subjects (ages 22 to 33 yrs) completed two separate 24-h chamber visits (one rest and one exercise day) and energy balance was maintained for each visit condition. On the exercise day, subjects completed 45-min of cycling at 57% Wattsmax (mean±SD, 72.8±5.8% VO2max) starting at 11:00 am. Activities of daily living were tightly controlled to ensure uniformity on both rest and exercise days. The area under the energy expenditure curve for exercise and rest days was calculated using the trapezoid rule in the EXPAND procedure in the Statistical Analysis Systems (SAS) and then contrasted.
RESULTS: The 45-min exercise bout resulted in a net energy expenditure of 519±60.9 kcal (P<0.001). For 14-h post-exercise, energy expenditure was increased 190±71.4 kcal compared to the rest day (P±0.001).
CONCLUSION: In young male subjects, vigorous exercise for 45-min resulted in a significant elevation in post-exercise energy expenditure that persisted for 14-h. The 190 kcals expended post-exercise above resting levels, represented an additional 37% to the net energy expended during the 45-min cycling bout. The magnitude and duration of increased energy expenditure following a 45-min bout of vigorous exercise may have implications for weight loss and management.

Background
In recent years there has been a focus on the calorie burn that occurs after training, referred to in science terms as EPOC (Excess Post-exercise Oxygen Consumption).  A variety of different types of training (usually revolving around brief duration, high-intensity methods such as interval training or circuits) have been proposed with the major effect of such activity being in the EPOC that is created.

Now, EPOC used to be thought to be related to something called the ‘oxygen debt’, essentially the difference in how much oxygen the body needed to sustain activity and how much was available.  We now know that it’s related to a host of other processes but these aren’t really that relevant practically.   What is relevant from a fat/weight loss point of view is how large the EPOC is and whether or not it contributes meaningfully to the overall caloric expenditure of an individual.

In a previous research review with the imposing title of Effects of Exercise Intensity and Duration on the Excess Post-Exercise Oxygen Consumption I examined a monster review on the topic.  In brief, looking at the data in aggregate, the study concluded that intensity was more important than duration in terms of the EPOC created.

And while the relative EPOC was higher for high-intensity activities (that is the percentage increase), the absolute level of EPOC was still pretty irrelevant  (maybe 30-50 calories).  The researchers concluded that the primary impact of exercise was still through the calories burned during the activity itself: the absolute EPOC was fairly irrelevant to the total whether an individual did long-duration low-intensity activity or short-duration high-intensity activity. Their conclusion based on review of the data was:

The manipulation of energy balance for these individuals should not be concerned with generating large EPOCs but focused on both the energy expended during the actual exercise and the design of programmes that enhance compliance.

The compliance issue is actually quite relevant given that the intensities used in the interval studies are generally exceedingly high and unlikely to be performed consistently by most people, especially beginning and/or overweight exercises (for more on this topic, please read Training the Obese Beginner series).  An added issue that I have talked about variously on the site is that many people looking for fat loss train daily; trying to perform high-intensity activity too frequently is a recipe for disaster. Read the previous review for the details, or the series on Steady State vs. Interval Training that it was a part of if you’re particularly bored.

However, several people have asked me about a recent study that seems to contradict the above conclusions, as it found that the EPOC following what was terms ‘vigorous’ activity was significant and it’s that paper I want to look at today.

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The Study

The study recruited 10 male subjects, aged 23-33 years who were capable of bicycling for 45 minutes continuously. Body composition was measured via DEXA and the subjects all underwent VO2 max testing using a fairly standard protocol.   The subjects performed two ‘exercise’ sessions.  In the first they sat quietly in a metabolic chamber (which was measuring the actual energy expenditure); in the second they performed actual exercise.

Basically, the ‘sitting quietly’ was the control condition, the researchers had to see how many calories the subjects burned at rest and in the following hours so that they could determine the difference in caloric expenditure and EPOC following the actual workout.

The exercise bout consisted of 45 minutes of cycling at 57% maximum wattage (so if a subjects Vo2 max wattage occurred at 300watts, they cycled at 171 watts), this put them at roughly 70% of their VO2 max; the duration was chosen to represent a midpoint for some rather standard recommendations for activity.  There was actually a short (4 minute) warm-up before the main set along with a short cool-down but I’m not going to bother detailing it.

Food intake was controlled (this can be a huge confound to a lot of these studies as eating raises metabolic rate and a lot of early exercise and metabolic rate studies confused the increase from eating with an increase from the exercise session); it is important to note that food intake was increased on the exercise day to maintain energy balance  That is, the subjects were not in a caloric deficit during the period following the training session.  The subjects stayed in the metabolic chamber following the exercise session and this is how the caloric expenditure following the exercise bout was determined.

For the exercise bout itself, the session burned a total of 519 +- 60 calories so about 11 calories per minute for the 45 minute exercise session.  This is consistent with it being a vigorous intensity.  And over the next 14 hours (including 3 hours of sleep), the subjects burned an additional 144+-50 calories with the majority of this occuring in the first 9 hours after training.

This EPOC represented a whopping 37% of the actual exercise bout energy expenditure, far higher than what was reported (usually 15% or less) in the LaForgia review I linked to above. And the total impact of the exercise bout was an energy expenditure of ~750 calories above and beyond the resting condition.  The researchers conclude

“The 24-h net energy expenditure difference between exercise and rest days was 750 kcal, a meaningful quantity over time if two or three such exercise bouts are inserted into the weekly schedule and energy intake is controlled.”

Fair enough.  And obviously a workout burning 750 cal/session (including the workout and EPOC) is meaningful.

My Comments

I think the first issue worth addressing is why this particular study found such a difference compared to previous studies, such as the ones reviewed in the LaForgia review I linked above.  Certainly there could be methodological differences; for example this study used a metabolic chamber to get an accurate measure of caloric expenditure, other studies using a different method (such as Douglas bags, think big balloon looking things that folks breathe in for later analysis) might be getting different measurements.

Another has to do with the fact that the subjects were deliberately kept in energy balance, and fed an additional 660 calories to offset the energy expenditure of the exercise bout.  The researchers state:

The increased energy intake balanced against energy expenditure (energy flux) has been shown in several studies to contribute to the elevated 24-h energy expenditure on exercise days or in trained individuals.

Of course this is a problem with applying this study to a dieting/fat loss situation where, by definition, you can’t be in energy balance (since you have to create a deficit by not replacing the exercise induced energy expenditure with more food).  This study needs to be re-done under conditions where energy intake isn’t increased to see if the same big increase in EPOC is seen under dieting conditions (as that would have actual application to fat loss).

The final reason that contributed is likely this: the workout tested in this study was  HARD.  As I noted above, subjects pedalled at a workrate equal to 57% of their max wattage output.  This corresponded to 70% of VO2 max which corresponds to roughly 85% of maximum heart rate.  Quite in fact, the heart rate measured during the activity was 163 +-16 beats per minute.  This would be equivalent to the Sweet Spot training that I discussed the Methods of Endurance Training series.  I’ll come back to this.

Without going into the details of what determines the EPOC, the researchers state fairly simply that:

The magnitude of post-exercise energy expenditure is greatest when the body experiences significant physiologic stress during prolonged and high intensity exercise.

Basically, through whatever specific mechanisms (and a host have been implicated), the ‘afterburn’ effect is maximized when you massively disrupt homeostasis in the body.  And this tends to occur most significantly for workouts that are a combination of high-intensity AND high-volume (i.e. long-duration).

Earlier studies tended to either look at EPOC following longer duration lower-intensity work or short-duration high-intensity work (such as interval training).   And the simple fact is that neither tend to cause the greatest homeostatic changes to the body: the longer duration stuff is too easy and the high-intensity stuff is too short.  In contrast, this workout had the subjects working pretty damn near their maximal steady state level (for most this falls around 170-180 beats per minute).  Anybody who has done this type of training can attest to the fact that it’s work.

Conceptually this is no different than what I discussed in the Reps Per Set for Optimal Growth where an intensity of 80-85% of maximum tends to put you in the range where you get both a high stimulus intensity coupled with a high-volume.  Higher intensities limit volume (both per set and per workout) and lower intensities don’t tend to have the stress associated with it even if you do more total volume.  Somewhere in the middle you get that maximal response where you combine high-intensity AND a high-volume.

In that vein, having done all three types of workout (long-durations at piss easy intensities, interval work of varying durations, long sessions at sweet spot), by far and away the sweet spot training is the hardest. Long duration/low intensity stuff is mainly just dull.  It’s not hard, just boring (and your butt gets sore on the bike).  The high-intensity stuff certainly hurts but the work bouts are too short for you to really suffer; the workout is over before it gets too awful.

In contrast, working for extended periods near your maximal output level is just uncomfortable as hell and the duration makes it pretty gruelling as the minutes tick slowly by.  This is even more true on a bike where the stress tends to be localized to the legs (running at this pace is a bit easier since the stress is ‘spread out’ a bit more.

That’s what this study did.  Most folks can go about an hour at their maximum steady state; this study was 45 minutes at maybe 90% of that intensity.  It’s a tough workout, most can’t do it that frequently and this would be more true if they are dieting and/or including weight training as well.  So while these study results are certainly interesting, and it’s clear that EPOC can be significant at least under the conditions tested (essentially energy balance and a hard/long workout), I find it questionable how relevant this is to real-world dieting situations where calories are lowered and/or weight training is being done.

AIM: The limited potential of exercise to induce weight loss could be partly due to the overestimation of the energy cost of exercise. The objectives of this study were twofold: 1) to investigate whether men and women are able to accurately estimate exercise energy expenditure (EE); and 2) to determine whether they have the ability to accurately compensate for the EE of exercise during a buffet-type meal.
METHODS: Sixteen (8 men, 8 women) moderately active (VO2 peak=45.4±7.7 mL.kg-1.min-1), normal weight (BMI=22.8±3.3 kg/m2) individuals, aged 20-35 years, were studied. They were blinded to two randomly assigned experimental conditions: a 200 and a 300 kcal (measured by indirect calorimetry) exercise sessions that were performed on a treadmill at the same intensity (50% of VO2 peak). At the end of each exercise session individuals were asked to estimate EE of the exercise sessions and to then eat the caloric equivalent of their estimated exercise EE from a buffet-type meal.
RESULTS: Estimated EE was higher than measured EE for both the 200 kcal (825.0±1061.8 vs. 200.1±0.7 kcal, P<0.05) and 300 kcal (896.9±952.4 vs. 300.2±0.7 kcal, P<0.05) sessions. Further, post-exercise energy intake was higher than measured EE for the 200 kcal (556.8±204.4 vs. 200.1±0.7 kcal, P<0.001) and the 300 kcal (607.2±166.5 vs. 300.2±0.7 kcal, P<0.001) sessions. Although post-exercise energy intake was lower than estimated EE, no significant differences were noted.
CONCLUSION: These results suggest that normal weight individuals overestimate EE during exercise by 3-4 folds. Further, when asked to precisely compensate for exercise EE with food intake, the resulting energy intake is still 2 to 3 folds greater than the measured EE of exercise.

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Background

Several years ago, a friend of mine (who now is of the two folks who runs Grassiron personal training here in Austin, look them up if you’re in town and want coaching) related a story of a phone conversation she’d had.  A girl had called and wanted to know how much exercise it would take to burn off a bag of M&M’s (calorie count about 270 calories).  When my friend told her the realities of it, the person asking the question apparently got annoyed and then outright angry about it.  Basically, she didn’t want to hear the reality of just how much activity it would take to burn off an exceedingly small amount of candy.

In a related vein, and this is something I’ve discussed in other articles on the site, it’s not uncommon in the least for people to drastically sabotage their own fat loss efforts by drastically overestimating the caloric expenditure from activity.   Folks will do an hour aerobics class (which really amounts to about 45-50 minutes by the time you factor in set ups and stuff) and say things like “I must have burned at least 1000 calories, I deserve a hamburger and milkshake.”  Or things of that nature.

Basically, along with many other reasons that exercise tends to have a fairly small impact on weight and fat loss (see Exercise and Weight/Fat Loss Part 1 and Exercise and Weight/Fat Loss Part 2 for a more thorough discussion), a gross over-estimation of the actual caloric expenditure from activity is one of them.

But while numerous studies have documented the degree to which folks tend to under-estimate their own food intake (a value ranging from 30-50% depending on the study and the group studied), no studies have actually examined the degree to which people overestimate the caloric expenditure from exercise. Which brings me to today’s study.

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The Paper

The study I want to review today actually wanted to examine two different, but related (and important) aspects of the issue I brought up above.  The first was the degree to which people over-estimated the actual caloric expenditure of exercise.  The second was the degree to which those same people compensated for the exercise bout in terms of how many calories they ate at a buffet type meal after the exercise bout.   They also wanted to see if there were any differences in macronutrient preference following exercise or between genders in terms of response.  Some psychological parameters were also measured but since no interaction with the actual results were found, I won’t detail them.

Towards that end, the study recruited 17 subjects (of which 16 completed the study; half were men and half were women) aged 20-35 who were ‘normal’ weight.  Body fat percentage ranged from 10% (the lowest male) to 42% (the highest female) who had only been moderately active (defined as no more than three hours exercise per week) and were weight stable for the past 6 months.

Subjects were tested for VO2 max (really VO2 peak, a distinction I won’t bother explaining here) and then put through two different exercise bouts at 50% of VO2 peak or about 65% max heart rate; this was done after they were all given a standardized breakfast meal.

The two different exercise bouts were measured (via indirect calorimetry) to burn either 200 or 300 calories and the women were tested a month apart to standardize for menstrual cycle (which can vastly impact on appetite as discussed in Impact of the Menstrual Cycle on Determinants of Energy Intake – Research Review.  The exercise bouts took on average, 28 minutes for the 200 calorie exercise bout and 44 minutes for the 300 calorie exercise bout.  So this was pretty moderate intensity exercise.

The exercise was done walking and the subjects were prevented from knowing how long they had walked (no clocks or watches could be seen) or having any way of knowing how many calories they had burned. Following each exercise session, subjects were asked to estimate how many calories they had burned, an hour later (after a shower) they were asked to consume food from a buffet type meal corresponding to their estimated exercise energy expenditure.  The chart below shows the results.

So the chart is showing the actual energy expenditure on the far left (200 calories on to the top, 300 calories on the bottom) along with the average estimated energy expenditures (825 calories on top, 896 calories on bottom) along with the actual energy intakes at the buffet meal (556 calories on top, 607 calories on bottom).  Basically, people overestimated the caloric expenditure of activity by about 4 fold while eating about 3 fold more than they burned.  But this actually doesn’t tell the whole story on the estimated energy expenditure since it’s only reporting the average overestimation.  Check this out.

For the 200 calorie exercise bout, the estimated energy expenditures, that is what subjects thought they burned ranged from 120 to 4000 calories.  No, that’s not a typo.  4000 calories. For the 300 calorie exercise bout, the range was 150 to 3000 calories.  Again, not a typo.  3000 calories.  Let that sink in, for exceedingly moderate amounts of activity, 30-45 minutes of brisk walking some of the study subjects thought they burned 3000-4000 calories.

I’d note that the researchers found no difference in macronutrient preference for the 200 vs. 300 calorie bout (no big shock there) and there was no difference between genders in terms of the response.  I’d also note that, in the discussion, the researchers discussed some of the limitations of the study.  First and foremost it was done on ‘normal’ weight individuals; whether or not it applies automatically to the obese is therefore unknown though I’d be surprised if it didn’t.

As well, whether or not a similar effect would be seen for high intensity activity is also currently unknown.   It actually wouldn’t surprise me if the effect weren’t actually larger for people performing high-intensity activity.  Contrary to what people believe, things like interval training actually do not burn that many more calories than moderate intensity activity because the caloric expenditure of the rest intervals brings the total way down. As well, interval sessions are usually far shorter; often interval sessions burn fewer calories.  But I’m not getting into the details of this here, read the Steady State vs. Interval Training series for more details.

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My Comments

In the simplest terms, this study had two major conclusions one of which we already knew about.   The first, and the ‘novel’ (at least in scientific terms) finding is that the average person probably sucks as much as estimating the actual caloric expenditure from activity as they do at estimating their actual energy intake from food.

You can see this all the time on Internet forums, people throwing out values for an hour of exercise that are simply fantasies.  They simply don’t realize that moderate activity burns maybe 5-10 cal/min (higher if you’re VERY VERY fit) so if you get 600 calories per hour you’re doing well.   Again, if you’re very well trained you can get higher values than this but well trained people aren’t usually the population we’re talking about when we’re looking at major weight and fat loss.

A secondary finding of this paper is that people still suck at estimating their actual food intake.  That is, even with their guesstimate at their actual energy expenditure, they still didn’t get close to it with their food intake at the buffet.  Recall that the subjects were instructed to consume food to balance out their guessed energy expenditure.  And they were still several hundred calories off.

Perhaps more importantly, the amount of food eaten after the activity was threefold what was actually burned during the activity itself.  As I mentioned in the background section above, this is common; people come out of fairly moderate amounts of activity thinking they’ve ‘earned’ a zillion calories and more than offset what they burned during activity.

At least one interesting thing to come out of this study was the absolutely massive variation between subjects in their estimated energy expenditure (remember it ranged from 150-200 at the low end to 3000-4000 calorie at the high end) and the researchers write:

The important inter-individual variation of estimated exercise EE likely results from the fact that some individuals have little knowledge with regards to the real energy cost of exercise and the energy value of food intake.  It would be tempting to speculate that the degree of over-estimation of the energy cost of exercise may relate to a poor exercise-induced weight loss response in some individuals.

That is, perhaps the people who get the least out of exercise in terms of weight/fat loss are the ones who drastically overestimate how many calories have been burned.  This requires further testing although it would be eminently logical.

I should finish by saying that this paper shouldn’t be taken as more ‘evidence’ that ‘exercise doesn’t work’ (though I have no doubt that some will use it as such).  Rather, people need to be educated and informed about how many calories exercise can realistically burn.  And the realities are that, except for highly trained athletes, the value is much lower than most think, hope or expect.  And certainly far less than the average person estimates it to be.

Background

It seems like I haven’t done a research review in forever, probably because I haven’t done a research review in forever.  But since I’m holding off on my little surprise until next week, this seemed as good a time as any.  Today’s paper is also something that is rather important in the overall scheme for folks seeking weight/fat loss.

Now, in the Training the Obese Beginner series, one comment that I made was that most studies have not found a massive impact on exercise in terms of increasing weight or fat loss and I outlined some of the reasons that was the case (mostly focusing on the generally low calorie burn).

That, of course raises the question

But even there, there are often some confusing things that occur in studies of exercise and weight loss, situations where the deficit created by exercise and the measured weight/fat loss aren’t the same.  That is, even where the exercise should have generated X fat loss but doesn’t.  A long-standing question has been why this is the case and there are a number of reasons for it.

One of them, of course, has to do with adjustments to the out side of The Energy Balance Equation that can occur when you perturb things; sometimes people are less active during the day due to fatigue or what have you from exercise.  This works to offset the apparent deficit.

Of course, there are other potential reasons not the least of which is an increase in food intake that occurs with exercise.  That is, while exercise can impact on the energy out side of the equation, it can also potentially impact on the energy in (food) side of the equation; the problem is figuring out which way exercise is going to impact things.

What I didn’t really address in the Training the Obese Beginner series is that, for some people, adding exercise works stunningly to generate fat loss.  That is, while the average response is often poor, averages tend to mask individual responses.  Some people do great but others do not.  Raising the question of what the individual differences between folks are.  One of those is how exercise impacts on appetite and hunger.

Until I write another overwritten series on the topic, simply realize that there can be a hugely variable response in whether or not someone gets appetite suppression (through physiological or psychological means) from exercise or an actual increase in activity.  Clearly, in the case where calories are not being controlled, if exercise causes someone to increase their food intake (for either psychological or physiological reasons), the predicted weight/fat loss will not occur.

And that brings us to today’s paper.

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The Paper

The paper wanted to address the question of why the obese don’t lose more weight in response to exercising by looking at how changes in the reward value of food changed with exercise; the basic assumption being that folks who showed a larger increase in the reward value of food would be more likely to overeat foods following exercise.

In looking at this, forty sedentary overweight and obese individuals (13 males, 27 females) with an average BMI of 31.03 and age of 39.3 years were recruited from a larger 12-week exercise study. The researchers examined the acute impact of exercise on the reward value of food before and following a 12-week supervised exercise program.

To assess food reward, the subjects were shown 20 pictures of foods varying in taste and macronutrient properties which were categorized according to sensory domain (sweet or non-sweet) and fat/carbohydrate content (high/low). Subjects rated the subjective reward value using a visual scale based on liking, wanting and preference.

I won’t detail this because it’s boring and uninteresting.  Results were tallied for each of the four food categories (high-fat/non-sweet, low-fat/non-sweet, high-fat/sweet, low-fat/sweet) to see if there were differences in changes in reward for different types of foods.  The foods were rated immediately before and after a single exercise bout (to see if exercise impacted on reward value) and both initially and 12-weeks later (to see if there were any long term changes).

Subjects were subjected to a 12 week exercise program where they burned 500 cal/session 5 days per week for a total of 30,000 calories over the total study length.  Weight loss was assessed and subjects were divided into responders based on their actual weight loss.

Only 34 of the original 40 subjects finished the study and of those there were 20 responders (6 of whom were males) and 14 non-responders (7 of whom were males) and the individual variability in net energy balance (based on measured weight loss) was massive (see graphic below).

Variability in Energy Balance

Subjects below the zero line were those that lost the expected amount of fat or more (based on estimations of the energy values of body fat and muscle) and were termed responders; those who are above the zero line lost less weight/fat than predicted and were termed non-responders.  The average responder lost nearly 5kg (10+ lbs) over 12 weeks, the non-responders a mere 1 kg (2.2 lbs).

And, as expected, there was a clear correlation between the actual changes in fat mass and the changes in reward value of foods.  The non-responders fairly consistently demonstrated a different response in terms of reward value for foods following activity with the largest impact being seen for (drum roll please) high fat and sweet foods.  In contrast, the responders showed either no significant response or a slight decrease in the reward value of the different categories of foods.

I’d note that after 12 weeks, even in the non-responders there was a difference by week 12, while they still showed a greater response in terms of reward value following activity compared to the non-responders, the effect was blunted compared to at the beginning of the study.  Something had changed in the physiology of the non-responders; it wasn’t eliminated mind you but the effect was blunted.

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My Comments

Clearly the results of this study (which I’d note fit into a previous data set showing that exercise tends to either have a neutral or increasing effect on food reward; it rarely has a negative effect) indicate one potential reason that exercise is either ineffective for weight/fat loss or, if not ineffective, shows such variable responses.

Clearly some people add activity to their regimen and lose fat/weight just as expected; others are frustrated.  And while there are other reasons that might cause this to occur, clearly an increase in food intake (and especially high-fat/sweet foods) would be a real problem.  Of course, this is only a real issue under conditions where calories/food intake isn’t being controlled.  Which is the condition that a lot of exercise studies are tested under.

Of some interest is that the impact of exercise on food reward is blunted after 12-weeks although it doesn’t appear to go away completely in the non-responders.  I actually mentioned in the Training the Obese Beginner series that I had often seen an increase in hunger in the first few weeks of exercise but that often switched itself as adaptations started to occur.  Clearly something may go on with long-term activity in terms of reprogramming some aspect of physiology.

If anything else, I think this study points out, yet again, that exercise is no magic pill for fat loss.  Clearly for some people, those who don’t find an increase in their hunger and liking for high-fat/sweet foods, exercise can work wonderfully even in the absence of explicit calorie control.  Since they don’t increase their food intake from high calorie/high-fat/sweet foods, they don’t get into trouble.   But for others, those who get an increase in the desire to eat such foods, there’s a problem.  Even if they don’t realize it, their increased desire for such foods will likely end up causing them to eat more after exercise sessions, eliminating the exercise induced deficit.  Results will be poor and they are likely to quit.

In that situation, either a diet that allows such foods in controlled amounts (e.g. a flexible dieting approach or something like Martin Berkhan’s Leangains) to be consumed or simply the realization that uncontrolled eating will cause problems has to happen.  Clearly left to their own devices, their bodies are causing problems.   They’ll have to find a workaround or count/control their food intake (at least until they stop getting such a pronounced effect).

And tune in next Tuesday for my surprise….

Background

There has long been a question of why some people seem to be able to ‘eat anything they want’ and remain thin while others can do no such thing; in fact this is often used as an argument that The Energy Balance Equation is wrong.

More in fact, the paper I’m going to talk about today was once trotted out by several individuals as ‘proof’ that The Energy Balance Equation was incorrect.  Unfortunately all their discussion really ended up proving was that, as I suggest in The Energy Balance Equation, the issue was not the equation, but that they had no clue what they were talking about.  But I’m getting ahead of myself.

Certainly we all have seen, known (or in lucky situations been) that person who seems to ‘eat anything they want’ without gaining appreciable weight.  This is in contrast to those people who seem to be able to simply look at food and get fat. What’s going on?

At least part of what’s going on, and this is outside of the paper I’m going to discuss today, is that these folks in question often don’t eat as much as you think they are.  Certainly you may see them gorging on food acutely (at a single meal, perhaps out with friends) but what you often don’t see is what they are doing the rest of the day, or the day before, or the day after.

So while you may see the single enormous meal, what you don’t see is the smaller or non-existent meals that they are eating at other times of the day.  Or the compensations that occur a day or two later to drastically reduce their food intake and keep them in energy balance in the long-term.  So while you may assume that they eat like that all day every day, you don’t know that for sure.

But as it turns out, that’s not all that’s going on.  As I discussed in The Energy Balance Equation one mistake people often make is assuming that the output side of the equation is static; that the energy output of a given individual is invariant over time.  Thus if you plug in X calories and the person doesn’t gain exactly Y weight, the equation must be invalid.  This is wrong for a bunch of reasons discussed in that article not the least of which being that the out side of the equation changes in response to cahnges in food intake, activity and obesity.

For example, in response to both increases and decreases in food intake (as well as body weight), we know that basal or resting metabolic rate (BMR/RMR) can go up and down.  Similarly, the thermic effect of food (TEF) is related to the amount (and type) of food being eaten and will adjust upwards or downwards as well.

Activity of varying sort can be affected by energy intake as well as body weight (e.g. larger bodies burn more calories in movement).  Clearly the idea that the out side of The Energy Balance Equation is unchanging is wrong.  Yet people keep pretending that it is when they simply look at calories in or out and what they think should happen to body weight without accounting for those changes.

But as it turns out, changes in the above three factors don’t seem sufficient to explain some of what is seen when people are overfed with studies finding a huge individual variance in how much fat is gained with identical amounts of overfeeding and that brings me in a very roundabout way to today’s paper; while over 10 years old, this was a seminal study that goes a long way towards explaining the odd observation that some people are seemingly able to ‘eat’ whatever they want and not get fat.   The researchers wanted to try to determine mechanistically what might be causing that to occur.

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The Paper

It’s been known for quite some time that people show a rather large amount of variability in terms of actual fat/weight gain in response to overfeeding and the researchers wanted to try to figure out some of the mechanistic reasons why this might be the case.

Towards this goal, the study recruited 16 people (12 males and 4 female) who underwent body composition measurement (via DEXA) and total energy expenditure (measured by doubly labeled water) who were then overfed by 1000 calories per day for 8 straight weeks.

I’d note that both basal metabolic rate and TEF were measured via indirect calorimetry, mainly to see if changes there could explain anything about the measured results.  As a control, subjects were required to maintain their exercise type activity at very low levels; this was done to prevent folks from trying to compensate for the increased caloric intake by simply exercising more.  While slightly artificial in terms of how people often work in the real world, this was simply a way of controlling the study to see what else might be going on.

Over the course of the study, an average of 432 cal/day was stored and 531 was dissipated through increased energy expenditure: this accounted for 97% of the total (note: this means that the energy equation was essentially balanced in that all calories were accounted for, either being stored or burned; none magically went anywhere else).  However, looking at the averages obscure what was really happening.

Moving to individual results, fat gain varied from a low of 0.36 kg (0.79 lbs) to 4.23 kg (9.3 lbs) a 10 fold variance despite the same 1000 calorie/day increase in energy intake.  Changes in BMR and TEF were unable to explain this difference.  BMR went up only 5% accounting for 8% of the extra energy while TEF went up 14%, simply in response to the increased food intake; none of those changes showed any correlation with changes in fat mass.   As I noted above, exercise type activity was clamped at low levels so changes there can’t explain the difference either.

And that brings us to NEAT, an acronym referring to Non-Exercise Activity Thermogenesis.  As the researchers define it:

NEAT is the thermogenesis that accompanies physical activities other than volitional exercise, such as the activities of daily living, fidgeting, spontaneous muscle contraction, and maintaining posture when not recumbent.

Basically, think of NEAT as the calorie burn associated with all activities that aren’t formal exercise.  And that’s where the researchers saw the massive difference between subjects; while the average increase in NEAT across all subjects was 336 cal/day, the individual changes in NEAT varied from -98 (that is it actually went down in at least one person) to +692 cal/day.

That is, in at least one subject, approximately 700 calories of the 1000 extra was burned off via NEAT.  That’s in addition to the increase in BMR and TEF which would have burned off even more of the total calories.  The researchers calculated that the increase in NEAT in the greatest responder would be the equivalent of strolling for 15 minutes per hour during waking hours.

In this vein, in the review of the Bodybugg/GoWearFit I mentioned that even small increases in activity over the course of the day can end up having a massive impact on overall energy balance because of how it can really add up.  The subjects with the increase in NEAT effectively had that happen without trying.

Of more importance, changes in NEAT directly predicted fat gain (or the lack thereof): people who showed the greatest increase in NEAT showed the smallest fat gain and vice versa.   I’d note in finishing out the paper that the four worst responders in terms of NEAT were the 4 female subjects; this really isn’t news inasmuch as we’ve also known for decades that women get the short end of the stick in terms of both weight gain and loss.

I’d also mention that this paper did nothing to determine the mechanisms behind NEAT (later studies have tried, and done poorly, at determining what is the actual cause of the increase in NEAT) only mentioning that NEAT seems to be a familial trait (suggesting a genetic basis).  Other later studies have shown that NEAT is essentially subconscious, people either do it or don’t.

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My comments:

There’s really not a huge amount to say about this paper; it’s a point of interest without a lot of practical application.  I only bring it up to make the point that many people’s assumptions about what does or does not disprove The Energy Balance Equation tend to stem with their misunderstanding of things; especially their failure to realize that the out side of the equation is not static.  And this goes especially for the NEAT component of energy expenditure, with individual increases in NEAT varying massively from one person to another.

Of more relevance, not only is the out side of the equation not static, there appears to be quite a bit of variability involved.  While some people get effectively no (or a negative) increase in NEAT with overfeeding, which makes their gaining of fat quite easy, others have essentially won the genetic lottery: in response to overfeeding, they subconsciously ramp up small calorie burning activities that add up over the course of the day to burn off the excess.

To beat that dead horse, the equation isn’t wrong, the out side of the equation in terms of NEAT simply differs massively between people especially in terms of the NEAT response.  The people who can apparently ‘eat like gluttons’ and not gain weight appear to have a physiological mechanism by which they burn off the excess, essentially protecting them from fat gain.

In that vein, I’d mention at least one other study that compared the response to overfeeding and dieting in terms of metabolic rate adjustment.  It found that those individuals who showed the greatest increase in metabolic rate to overfeeding showed the least drop in response to dieting; by contrast those people who showed the least increase to overfeeding showed the biggest drop with dieting.

That study posited the existence of spendthrift (big increase with overfeeding/small decrease with dieting) and thrifty (small increase with overfeeding/big decrease with dieting) physiologies.  Clearly the first has as huge benefit in terms of both avoiding weight gain as well as losing it if necessary; the second group will have a much larger problem.

As I mentioned above, follow up work to this seminal paper has done little to determine the mechanisms behind it (which might lead to some way of increasing NEAT in those not disposed to it).  It appear to be genetic and more or less subconscious.  Of course, that doesn’t stop people from consciously trying to do things to increase their activity levels and energy expenditure outside of formal exercise.  All of the old behavioral strategies such as taking the stairs instead of the elevator, parking further away, etc. all end up adding up over time.

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Chaput JP, Tremblay A. Obesity and Physical Inactivity: The Relevance of Reconsidering the Notion of Sedentariness. Obes Facts. (2009)2(4):249-254.

The population statistics of most countries of the world are indicating that industrialization and computerization have been associated with an increase in sedentariness and more recently with a significant shift from healthy weight to overweight. In general, this change in the overweight/obesity prevalence is attributed by health professionals to suboptimal diet and physical activity practices. However, recent data raised the possibility that excess weight gain might also be the outcome of changes imposed by our ’24-hour’, hectic lifestyle. Parallel to an increase in body weight, one has observed a reduction in sleep time and an increase in knowledge-based work (KBW) that appear as a growing necessity in a context of economic competitiveness and globalization. Sleep and cognitive work both exert a trivial effect on energy expenditure and may thus be considered as sedentary activities. However, their respective effect on energy intake is opposite. Indeed, an increase in the practice of the most sedentary activity, i.e. sleep, is associated with a hormonal profile facilitating appetite control whereas KBW appears as a stimulus favoring a significant enhancing effect on food intake. Television viewing is another example of sedentary activity that has been shown to increase the intake of high-density foods. These observations demonstrate that the modern way of living has favored a change in human activities whose impact goes well beyond what has traditionally been attributed to a lack of physical exercise. Therefore, we will need to reconsider the notion of ‘sedentariness’ which includes several activities having opposing effects on energy balance.

My comments: Traditionally, the treatment of obesity has focused on two primary components which are dietary intake and energy output; there are both good and bad reasons for this that I’ll address in a future article or research review but the fact remains that those two factors tend to represent the things we have the most control over (e.g. we can’t do anything about genetics, or about what mom did while she was pregnant).  I tend to think I’ve spent enough time on the site talking about diet that I needn’t get into it here so I’m going to focus on the activity end of things.

Now, as discussed in Metabolic Rate Overview as well as The Energy Balance Equation, there are 4 primary components on the energy out side of the energy balance equation: Basal metabolic rate (BMR), Thermic effect of Food (TEF), Thermic Effect of Activity (TEA) and Spontaneous Physical Activity/Non-Exercise Activity Thermogenesis (SPA/NEAT).  The two I’m going to focus on relative to today’s research review are the last two, a recent separation whereby formal exercise and all other daily activities have been separated out.

Now, traditionally obesity treatment has also focused on the exercise end of the equation but there have always been a few problems with this.  Perhaps the largest relative to what I want to talk about today (and I’ll be doing a very thorough article at a later date on this so please be patient in the comments) is that the amount of exercise that is or can usually be done is actually fairly trivial compared to the rest of the day.

That is, the hour someone might spend engaged in exercise is still pretty small compared to what’s happening the other 23 hours of the day.  And, as many have found out by using tools such as the Bodybugg/GoWear Fit, small changes during the majority of the day (e.g. getting up every so often during the day to walk around at work for 8 hours) end up having a far larger impact on daily energy expenditure compared to the hour of exercise they might do.  As many have also found, being very inactive for those same 8 hours (e.g. jockeying a computer desk) doesn’t burn many more calories than laying in bed.

Which is all a very long introduction to today’s paper which looks in some detail at two of the major changes in modern life that contribute to our overall ‘inactivity’ during the day: sleep and what the researchers decided to call knowledge based work (KBW).  Sleep is fairly explanatory but, by KBW, they are referring to things such as school, jobs involving thought and concentration and even potentially video games.  Basically anything where you’re sitting on your ass for most of it but having to involve your brain rather intently.

And while both activities fall under the heading of ‘inactivity’ (in that you burn very few calories during either of them), they actually end up having not only different but diametrically opposed effects on the potential for weight gain and obesity.  Basically, just saying that ‘inactivity causes weight gain’ is simplistic and, as it turns out, incorrect.  The type of inactivity is relevant here.

In the case of sleep, and there has been a tremendous amount of literature in this regards in recent years, it’s turning out that sleep deprivation does rather horrible things not only for overall health but for weight gain and obesity risk.  Sleep deprivation tends to decrease leptin level (removing the tonic ‘block’ that leptin exerts on appetite/hunger) and raise level of ghrelin (the only hormone shown to directly stimulate hunger in humans).   I discuss both hormones in detail in the series on Hormones of Bodyweight Regulation but the end result of such a shift will be an increase in hunger/appetite along with a negative effect on calorie partitioning.  Hormones such as cortisol, thyrotropin hormone (involved in thyroid function) and others are also impacted positively by sufficient sleep and negatively by too little sleep.  As the authors state:

Hence the beneficial effect of sleep go well beyond its role in the restoration and maintenance of tissue structure and function…Despite the low energy cost of sleep, population studies have repeatedly shown that a short average duration of sleep is associated with excess body weight…recent research evidence showed that an average nightly sleep of 7-8 h in adults is associated with a lower risk of obesity, type 2 diabetes, coronary heart disease and all-cause mortality.

Hard to get much clearer than that.  Basically, despite the unbelievably low caloric cost of sleeping (usually around 1 cal/min), the indirect impact is massive in terms of the benefits for getting enough sleep and harm for not.

Moving on to the other topic of the paper we get to KBW, again referring to activities such where you’re sedentary but engaged in large amount of mental activity.  The paper mentions work, school, even video games and computer ‘chatting’ (you Facebook people know who you are) and other related activities as potential examples of KBW.

And, as you might expect, while similarly sedentary like sleeping, the impact of KBW on appetite and body weight regulation tend to be rather negative.  The brain, unlike skeletal muscle, can’t use fat for fuel and studies have shown that intense thinking can screw blood glucose levels; this is relevant as some work shows that falling or lowered blood glucose can stimulate hunger.  And usually for junkier food (which is invariably found in large amounts in the work space).

Studies have found that even short bouts of intense KBW can increase total energy and fat intake as well.    In one, for example, females were assigned to a 45-min mental work session and then provided an ad-lib buffet.  Despite only burning 3 extra calories during the task, the KBW group ate 229 more calories compared to a group that only rested.  In the long-term, this adds up big time.

And while it hasn’t been studied directly, the researchers question whether such things as video games and Internet chatting might be similarly stimulatory of appetite (and let’s be honest, is anybody eating non-junk food when they play WoW).  The amount of time spent watching Tv is a known risk factor for obesity in children, increasing the intake of high-energy density tasty foods; whether this is related to the same mechanism as KBW such as work or studying is currently unknown.

Finally, it’s worth mentioning that Tv and computer involvement is often done late at night and this can interrupt sleeping patterns (the constant influx of photos into the eyes makes it harder to get to sleep).  So there’s a potential double whammy.

Summing up: So that’s that, a quick look at two different types of ‘inactivity’ that end up having diametrically opposed effects on the risk for weight gain, obesity and other health risks. Those two are sleep, perhaps the most sedentary activity of all (unless you get lucky) and knowledge based work (KBW).

Getting sufficient sleep, something that is becoming harder for many to do (by choice or life requirement) is a key aspect of not only overall health but limiting obesity risk.  With good sleep hygiene and habits (e.g. get off the computer earlier, go to bed a bit earlier every night), this is at least within the realm of some people’s control.

The issue of KBW is tougher as folks have to make a living and many jobs involve long hours of KBW (often in an environment where nothing but crappy food is available).  Clearly quitting your job and sleeping all day, while attractive, isn’t an option for most.  At least being aware that intense bouts of KBW can screw with blood glucose and appetite may help with finding strategies around it.

Keeping better snack foods handy to stave off hunger following such work efforts would be one strategy, I have to wonder if a small amount of carbohydrate during the activity would help to stabilize blood glucose.  Perhaps Gatorade can come up with a ‘Conference Call Gatorade XXXtreme’ version of their drink.  Yes, XXXtreme with three X’s.

With the sheer volume of research appearing on a weekly basis, this will at least help me to look at data in a more timely fashion.  I’d mention that, for anyone who wants an even better look at a lot of studies, you’d be well served to consider Alan Aragon’s monthly Research Review which I reviewed in the confusingly titled Alan Aragon Research Review – Product Review.

In any case, today I want to look at two recent studies which are:

1. Deglaire et al. Hydrolyzed dietary casein as compared with the intact protein reduces postprandial peripheral, but not whole-body, uptake of nitrogen in humans. Am J Clin Nutr. (2009) 90(4):1011-22.
2. West et. al. Elevations in ostensibly anabolic hormones with resistance exercise enhance neither training-induced muscle hypertrophy nor strength of the elbow flexors. J Appl Physiol. 2009 Nov 12.

For each study I’ll give a brief background to the topic, look at what was done and then jump straight to the conclusions with some final summing up.  As noted above, some of the detail will be left out but I figure that anyone who is that interested in the details of methodology and such will simply get ahold of the full paper and read it themselves.

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Deglaire et al.  Hydrolyzed dietary casein as compared with the intact protein reduces postprandial peripheral, but not whole-body, uptake of nitrogen in humans. Am J Clin Nutr. (2009) 90(4):1011-22.

BACKGROUND: Compared with slow proteins, fast proteins are more completely extracted in the splanchnic bed but contribute less to peripheral protein accretion; however, the independent influence of absorption kinetics and the amino acid (AA) pattern of dietary protein on AA anabolism in individual tissues remains unknown. OBJECTIVE: We aimed to compare the postprandial regional utilization of proteins with similar AA profiles but different absorption kinetics by coupling clinical experiments with compartmental modeling. DESIGN: Experimental data pertaining to the intestine, blood, and urine for dietary nitrogen kinetics after a 15N-labeled intact (IC) or hydrolyzed (HC) casein meal were obtained in parallel groups of healthy adults (n = 21) and were analyzed by using a 13-compartment model to predict the cascade of dietary nitrogen absorption and regional metabolism. RESULTS: IC and HC elicited a similar whole-body postprandial retention of dietary nitrogen, but HC was associated with a faster rate of absorption than was IC, resulting in earlier and stronger hyperaminoacidemia and hyperinsulinemia. An enhancement of both catabolic (26%) and anabolic (37%) utilization of dietary nitrogen occurred in the splanchnic bed at the expense of its further peripheral availability, which reached 18% and 11% of ingested nitrogen 8 h after the IC and HC meals, respectively. CONCLUSIONS: The form of delivery of dietary AAs constituted an independent factor of modulation of their postprandial regional metabolism, with a fast supply favoring the splanchnic dietary nitrogen uptake over its peripheral anabolic use. These results question a possible effect of ingestion of protein hydrolysates on tissue nitrogen metabolism and accretion.

My Comments: Ever since the pioneering work in the 90′s on fast and slow proteins, there has been continued interest in the digestion speed of proteins and how that impacts on metabolism, performance and, of course, muscle growth. In recent years, there have been many claims made for the superiority of faster proteins to slower in terms of ‘speeding amino acids to muscle’ in terms of promoting growth.

As well, as many may note, a recent commercial product (T-nations Anaconda), who’s anabolic claims were analyzed in perhaps the most commented article on the site in Alan’s Aragon’s guest article Supplement Marketing on Steroids, has recently been released to the market.

For background, hydrolysates are simply whole proteins that have been pre-digested (through the addition of enzymes during production) to some degree.  The theory being that, due to this pre-digestion, the hydrolysate will be digested in the stomach faster, getting aminos into the bloodstream faster and, presumably, having a better effect on skeletal muscle than slower proteins.

But is it true?  Guess.

The above study examined this issue by feeding 21 subjects 2 test meals containing ~26.5 grams of either intact casein or it’s hydrolysate; the protein had been marked with radioactive nitrogen so that it’s fate after ingestion could be tracked over the next 8 hours.  The test meals also contained 96 grams of carbohydrate and 23 grams of fat; this is worth noting as adding other nutrients to fast proteins often makes them behave more like slow proteins.  I’ll spare you the methodology, sufficed to say that tracking protein after it enters the body is brutally complicated and involves a lot of modelling and various measurements of blood amino acid levels and such.

Here’s what the study found.  Over the time course studied (8 hours after ingestion), the hydrolyzed casein product showed greater losses from digestion (that is, less was absorbed).  As well, a greater amount of the hydrolysate was oxidized for energy through deamination (a process by which the amino group is stripped off the carbon backbone).  Finally, a larger amount of the casein hydrolysate was used by the splanchnic bed (gut and intestines) with significantly less of the total protein reaching the bloodstream or peripheral tissues (muscles).

To quote the researchers:

Despite similar overall net postprandial protein utilization, our results indicate important differences in metabolic partitioning and kinetics between protein sources characterized by a preferential utilization of dietary nitrogen by for splanchnic protein syntheses after HC [hydrolyzed casein] ingestion at the expense of the incorporation into peripheral tissues.

Translating that into English: hydrolyzed casein is digested more poorly, gets burned for energy to a greater degree and gets used more by the gut than intact casein; the end result of this is that hydrolyzed casein provides LESS amino acids to skeletal muscle after ingestion than intact casein protein.

So not only is the claim that hydrolysates are better at providing aminos faster to skeletal muscle wrong, the reality is actually exactly reversed: intact casein is better for providing aminos to the muscle.  I’d note that other studies have found this as well: in one, intact protein provided MORE branched-chain amino acids into the bloodstream than a hydrolyzed form.

I’d add to this that, as I discussed in The Protein Book, other data supports the idea that slower proteins may actually be superior to faster proteins for muscle growth; in one set of studies, for example, milk protein (a mix of slow and fast proteins) resulted in greater hypertophy than soy (a fast protein) over 8 weeks of training and supplementation.  As well hydrolyzed proteins tend to taste like bleach; it’s no coincidence that Anaconda has to come with a separate flavoring intensifier: hydrolysates are gag-inducing.  They can’t be consumed straight.

Summing up: Hydrolysates are not only not superior to intact protein in terms of providing amino acids to skeletal muscle, they are distinctly inferior.  Their fast digestion speed leads to greater digestive losses, more oxidation via deamination and provides less amino acids to skeletal muscle.  That’s on top of tasting like vomit.  Or at least making you want to.

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West et. al. Elevations in ostensibly anabolic hormones with resistance exercise enhance neither training-induced muscle hypertrophy nor strength of the elbow flexors. J Appl Physiol. 2009 Nov 12.

The aim of our study was to determine whether resistance exercise-induced elevations in endogenous hormones enhance muscle strength and hypertrophy with training. Twelve healthy young men (21.8 +/- 1.2 y, BMI = 23.1 +/- 0.6 kg(.)m(-2)) independently trained their elbow flexors for 15 weeks on separate days and under different hormonal milieu. In one training condition, participants performed isolated arm curl exercise designed to maintain basal hormone concentrations (low hormone, LH); in the other training condition, participants performed identical arm exercise to the LH condition followed immediately by a high volume of leg resistance exercise to elicit a large increase in endogenous hormones (High Hormone, HH). There was no elevation in serum growth hormone (GH), insulin-like growth factor (IGF-1) or testosterone after the LH protocol, but significant (P < 0.001) elevations in these hormones immediately and 15 and 30 min after the HH protocol. The hormone responses elicited by each respective exercise protocol late in the training period were similar to the response elicited early in the training period indicating that a divergent post-exercise hormone response was maintained over the training period. Muscle cross-sectional area increased by 12% in LH and 10% in HH (P < 0.001) with no difference between conditions (condition x training interaction, P = 0.25). Similarly, type I (P < 0.01) and type II (P < 0.001) muscle fiber CSA increased with training with no effect of hormone elevation in the HH condition. Strength increased in both arms but the increase was not different between the LH and HH conditions. We conclude that exposure of loaded muscle to acute exercise-induced elevations in endogenous anabolic hormones enhances neither muscle hypertrophy nor strength with resistance training in young men. Key words: testosterone, growth hormone, IGF-1, anabolism.

My Comments: For several decades now, there has been intense focus on the acute hormonal response to training.  This started back in the 80′s where researchers, interested in growth did a rather cursory examination of elite powerlifters and bodybuilders, made some assumptions about muscle size, made some even bigger assumptions about how they trained, and then proceeded to reach some staggeringly poor conclusions.

Basically, what they observed was that bodybuilders were bigger than powerlifters, which is debatable in the first place.  They also observed that powerlifters typically used low reps and long rest periods and bodybuilders (remember: this was the Arnold era) trained with high reps and short rest periods.  Thus they concluded that high reps and short rest stimulated muscle growth and went looking for reasons why this was the case.  I’d note that this is not really how you’re supposed to do science: you don’t reach your conclusion and go find reasons why it’s right.  You test hypotheses and draw your conclusions from that.  But I digress.

And the main focus for a while was potential differences in hormonal response to training, primarily focusing on testosterone and growth hormone (GH).  The basic study design that was followed was to compare the acute hormonal response to either 3 sets of 5 repetitions with a long rest interval (3 minutes) to sets of 10 with a 1 minute rest interval.  Repeatedly, studies showed that the first type of training boosted testosterone and the second GH.  Entire training schemes have grown out of this but there was a problem: nobody ever bothered to see if these acute (usually less than 10-15 minute) bumps in hormones actually did anything.

Nevermind that this makes little sense anyhow for a variety of reasons.  Not the least of which is that women have higher GH levels than men and get a bigger GH response to training, yet they don’t grow better.  If anything, with the known impact of testosterone on muscle growth, if there was to be any benefit to this, you’d expect the lower rep/heavy work to be superior.  Yet the researchers were arguing that it wasn’t.   There was a logic missing in the argument (not the least of which being the assumption that powerlifters had smaller muscles than bodybuilders) that seemed to get skipped over.

In addition to the science, there is a long held belief, echoed in various places (including the comments section of another contentious article I wrote titled Squats vs. Leg Press for Big Legs) that certain movements, notably squats and deadlifts, will have full-body growth stimulating properties, generally mediated through the hormonal response.

It’s not uncommon to see people recommending things like “If you want big arms, squat/train legs.” for example.  Essentially, heavy leg work is touted as being the key to overall growth.  Nevermind that the same people who make this argument will often complain about “All those guys in the gym with huge upper bodies and no legs” without realizing that the two ideas contradict one another (that is, if leg training is required for growth, how can guys get huge upper bodies without training legs).  But I digress again.

In any case, this study examined the issue directly with a somewhat confusing study design: twelve healthy young men trained their biceps on different days of the week under different training conditions.  In what they called the low-hormone condition, the biceps were trained all by themselves; no other exercise was done.  In the other called the high-hormone condition, the biceps were trained and then a large-volume of leg training was done to elevate the supposedly anabolic hormones.

Does that make sense, all subjects trained both arms, but on different days and under different conditions.  And the training was far enough apart that the hormonal response from the leg training wouldn’t have impacted the low-hormone training session.  This training was followed for 15 weeks and subjects consumed protein both before and after the training (so there was nutritional support).

Hormone levels were measured and while there was no significant change in hormones in the low-hormone situation, in the high-hormone situation, there were increases in lactate, growth hormone, free and total testosterone and IGF-1 with the peak occurring approximately 15 minutes after the leg work.

And, if the hormonal response to heavy leg training actually has any impact, what you’d expect to see is that one arm, the one trained along with the leg training, would grow better.

Did it happen? Guess.

Both maximal strength and muscle cross sectional area increased identically in both arms to the tune of a 20% vs. 19% increase in strength for low- vs. high-hormones and an increase in skeletal muscle cross sectional area of 12% vs. 10% in low- vs. high-hormones.  These differences were not statistically significant. Quoting the researchers:

Despite vast differences in hormone availability in the immediate post- exercise period, we found no differences in the increases in strength or hypertrophy in muscle exercised under low or high hormone conditions after 15 weeks of resistance training. These findings are in agreement with our hypothesis and previous work showing that exercise-induced hormone elevations do not stimulate myofibrillar protein synthesis (36) and are not necessary for hypertrophy (37). Thus, our data ((36) and present observations), when viewed collectively, lead us to conclude that local mechanisms are of far greater relevance in regulating muscle protein accretion occurring with resistance training, and that acute changes in hormones, such as GH, IGF-1, and testosterone, do not predict or in any way reflect a capacity for hypertrophy.

I don’t think it gets any clearer than that and I’d note that another recent study titled “Resistance exercise-induced increases in putative anabolic hormones do not enhance muscle protein synthesis or intracellular signalling in young men.” by the same group found the exact same thing.

Summing Up: Leg training has no magic impact on overall growth, most of which is determined locally (through mechanisms of tension and fatigue mediated by changes in local muscular metabolism).  If you want big arms, train arms.  If you want big legs, train legs.

And if folks are wondering why empirically ‘folks who train legs hard’ seem to get big compared to those who don’t, I’d offer the following explanation: folks willing to toil on heavy leg work work hard.  Folks too lazy to train legs hard often don’t.  And it’s the overall intensity of the training that is causing the difference, not the presence or absence of squats per se. Which is why guys who only hammer pecs and guns get big pecs and guns even if they couldn’t find the squat rack in the gym: the small acute hormonal responses to training are simply irrelevant to overall growth.

McClave SA, Snider HL. Dissecting the energy needs of the body. Curr Opin Clin Nutr Metab Care. (2001) 4(2):143-7.

The majority of the resting energy expenditure can be explained by the energy needs of a few highly metabolic organs, making up a small percentage of the body by weight. The relationship of the specific size, individual metabolism, and proportional contribution to the actual body weight and total energy expenditure for each of these organs is a dynamic process throughout growth and development, the onset of disease, and changes in nutritional status. Defining the energy needs of the individual tissues and organ systems immeasurably enhances our understanding of the body’s response to these clinical processes, which otherwise could not easily be evaluated by focusing solely on total energy expenditure, fat-free mass, nitrogen imbalance, or actual body weight. Recently reported studies have served mainly to reinforce concepts described previously, and clarify some areas of controversy.

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Background

Last month, I answered a Q&A on Reducing Body Fat Percentage by Gaining Muscle and in that article I mentioned that the actual caloric burn of skeletal muscle is actually quite low compared to what is often claimed.  In the comments section someone mentioned a recent seminar where the value of 50 cal/lb for muscle was thrown out and asked for clarification on my claim.

Unlike previous research reviews, today’s paper isn’t an actual study but rather a review paper so my discussion will be a little bit different in terms of what I want to look at.  The paper itself is actually fairly technical and I don’t want to focus so much on the technical aspects as on the concepts and implications that the paper deals with as they pertain to issues of body composition.

More specifically I want to look at some of the common claims that are often thrown around in the world of body composition such as “Adding muscle mass significantly raises metabolic rate.” and “Fat cells burn no calories, they are metabolically inert.”  While this paper was examining the issue from a different perspective, it actually provides good data on both questions.

Specifically today’s paper examines in some detail how different tissues of the body (e.g. muscle vs. fat vs. organs) contribute to the body’s resting energy expenditure.  As well, factor such as disease, growth/development and under-nutrition are examined in terms of how they impact on different tissues in the body and their energy expenditure.

As I discuss in detail in Metabolic Rate Overview, there are four primary components to total daily energy expenditure: Resting Energy Expenditure (REE), Thermic Effect of Activity (TEA), Thermic Effect of Food (TEF) and Non-Exercise Activity Thermogenesis/Spontaneous Physical Activity (NEAT/SPA).

Of those four, resting energy expenditure plays the major role in total daily energy expenditure, generally comprising 65-70% of the total.  So looking at the differential impact of each tissue on REE tends to give  pretty decent picture of what’s going on.

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The Paper

The paper begins with an introduction to the overall concepts, pointing out estimating REE in individuals of different body sizes has been classically difficult. While body weight per se is a decent indicator, REE actually tends to scale better with body surface area.  However, this gives no indication of which tissues (and in what proportion) are contributing to overall REE.

Readers may have seen the statement that ‘The largest predictor of REE is lean body mass” and there is certainly some truth to that.  However, lean body mass (aka fat free mass) only predicts 53-88% of the variability in energy expenditure.  There are a number of reasons for this not the least of which being that lean body mass/fat free mass is not a single homogeneous tissue.

Rather, as discussed in What Does Body Composition Mean, lean body mass represents organs, skeletal muscle, bone, skin and basically everything in the body that isn’t fat mass.  And as you’ll see shortly, each of those tissues burns very different numbers of calories on a day to day basis.  Which means that variability in the amounts and proportions of those tissues will impact on overall resting energy expenditure.

Next the paper discusses the different methodologies used to estimate the resting energy expenditure of different tissues.  I don’t want to get into huge detail on this. Suffice to say that newer technology has allowed for more and more accurate methods of estimating the caloric expenditure of different tissues in the body.

While they are still not error-free (nothing in science ever is), some of the newer methods of measurement may explain why some of the oft-held beliefs about caloric expenditure and values that are often thrown out are turning out to be wrong.   Of course that also means that future developments may render current values incorrect.

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The Normal Human

The next topic addressed in the paper is an examination of the different tissues and how they contribute to resting energy expenditure in a fairly ‘average’ human being.  I’ve reproduced Table 1 from the paper below, honestly this was the main reason I wanted to examine this paper, to get this chart up on the site.

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Other refers to bone, skin, intestines and glands.
Note: the lungs have not been measured for methodological reasons but have been estimated at 200 kcal/kg similar to the liver.

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As you can see above, and quite contrary to what is commonly stated, skeletal muscle actually has a fairly low resting energy expenditure, roughly 6 calories per pound.  This is contrast to very old values of 100 calories/pound or even more recent claims that a pound of muscle will raise metabolic rate by 40-50 calories per pound.

Additionally, an in contrast to what is commonly claimed, fat cells do burn calories.  Admittedly the value is not massive (roughly 2 calories per pound) but the idea that fat cells are completely inert is also incorrect.  We now know that fat cells produce a variety of hormones, etc. (e.g. leptin, adiponectin) and that expends calories.  Again, not much per unit mass of fat, but for someone carrying a lot of fat mass, this does add up.

Perhaps of more relevance, and getting back to the paper per se, the primary contributor to resting energy expenditure comes from the organs with the liver, heart, kidneys and brains contributing roughly 70-80% of total resting energy expenditure.  This is despite the fact that they only make up approximately 7% of total body weight.  That is, despite their relatively small weight, they are simply massively metabolically active on a day to day basis.

In contrast, while skeletal muscle may contribute roughly 40% of total weight (a little bit less in women), it only contributes 28% of total resting energy expenditure.  Essentially, the relatively small caloric burn of a single pound of muscle mass is made up for by the sheer quantity of it.   Which doesn’t change the fact that adding muscle mass still won’t have a massive impact on resting energy expenditure.

To put that into mathematical perspective, gaining 20 pounds of muscle would be expected to increase resting energy expenditure by approximately 120 calories per day.  Certainly that does have an impact overall (equivalent to perhaps 10 minutes per day of moderate intensity cardio) but also keep in mind the time frames involved to gain that much muscle mass.  Expecting that adding a bit of muscle to have massive impacts on metabolic rate in the short-term is simply unrealistic; a few pounds gained simply won’t have any major impact.

Rather, I would expect that any real impact of building muscle mass on The Energy Balance Equation is going to come through the training done to stimulate/maintain muscle mass increases along with the caloric cost of building the muscle in the first place.   But once it’s there, the caloric expenditure at rest of skeletal muscle is simply very low.

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Factors Affecting Energy Expenditure

Having examined the average contribution of different tissues to the body, the researchers then look at a host of other topics, only a few of which I’m going to really look at in any detail.

Growth and development is covered first, examining how the ratios of energy expenditure to body weight changes over the lifespan.  Since most reading this are full grown adults, the changes that occur from childhood to maturity don’t seem that relevant.

One issue of some importance is covered next and that’s the effect of differences in body size between individuals.  In general, if you look at two people of different body sizes, larger folks tend to have lower resting energy expenditures relative to their body mass.  This is most likely related to differences in the proportion of organ weight (recall from above that the organs contribute the most to overall resting energy expenditure) to total body weight.

Meaning this: on average, organ weight won’t vary much between individuals.  So if one person is larger than another, that difference in size is likely to occur through changes in either muscle mass or fat tissue, neither of which makes massive contributions to resting energy expenditure (and differences in body composition won’t have nearly the impact that most think given the relatively small difference in caloric expenditure between muscle mass and fat mass).

Practically, this means that equations that estimate resting energy expenditure based solely on body weight will tend to overestimate larger individuals to some degree.  Of course, as I recently discussed in Adjusting the Diet, since all estimates of energy expenditure and/or caloric intake have to be adjusted based on real-world changes anyhow, I’m not sure how important this is practically.

I should probably address a question that I imagine will come up in the comments, given the enormous variability in energy expenditure per pound of tissue, where does the quick estimate of 10-11 calories/pound (22-24 cal/kg) come from?  And the answer is that it’s basically a weighted average of the above values.  That is, if you took the values for caloric expenditure/unit weight times their contribution to overall weight and worked it out, you’d get a value that was pretty close to the quick estimate value.  Again, this will tend to vary based on actual body size due to differences in the relative contribution of each tissue to the body’s total weight.

Next the researchers looked at the impact of both undernutrition and refeeding on energy expenditure at rest.  During underfeeding, they point out that skeletal muscle and fat are generally the major tissue lost while organs are spared.  This tends to have the impact of raising the relative proportion of energy expenditure to body weight (because the low energy expenditure tissues are being lost).  Of course, with extended dieting, there is also an adaptive component of metabolic rate reduction as all tissues in the body tend to slow their overall energy expenditure.

In contrast, during refeeding, there is often a hypermetabolic state that occurs, possibly due to increases in protein synthesis, core temperature and the thermic effect of food.  As well, there are a number of hormonal effects that occur when calories are raised, a topic I discuss in more detail in The Full Diet Break, all of which may have potentially beneficial impacts on overall energy expenditure and metabolic rate.

Finally the researchers examine the impact of disease and injury on energy expenditure but I don’t find that terribly relevant to this article.

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Summing Up

The main point that I wanted to make with today’s research review was to clear up some of the oft-held (and unfortunately incorrect) ideas regarding the impact of things like skeletal muscle mass and fat mass on resting energy expenditure.  Based on current data, the idea that skeletal muscle burns massive numbers of calories would appear to be 100% incorrect.

Rather, skeletal muscle actually burns fairly few calories on a per pound basis; it primarily has a major impact on resting energy expenditure because there is a good bit of it.  But adding even moderate amounts of muscle are unlikely to massively impact on energy expenditure.  As noted above, I expect the major effect to be from the effort of stimulating muscle mass gains along with the energy needed to synthesize that muscle tissue.  But once it’s there it doesn’t burn many calories.

Rather, the majority of resting energy expenditure is generated by the organs which, despite their small size, burn a massive number of calories per unit weight.  Someone on the support forum jokingly asked “So how do I hypertrophy my liver?”

Finally, fat cells, while not having much of a calorie burn do burn calories.  In fact, they are only about 1/3rds of the burn of skeletal muscle (2 cal/lb vs. 6 cal/lb respectively).  While low, someone carrying a lot of fat will have this add up and it will contribute to overall resting energy expenditure.

Stallknecht B et. al. Are blood flow and lipolysis in subcutaneous adipose tissue influenced by contractions in adjacent muscles in humans? Am J Physiol Endocrinol Metab. 2007 Feb;292(2):E394-9.

Aerobic exercise increases whole-body adipose tissue lipolysis, but is lipolysis higher in subcutaneous adipose tissue (SCAT) adjacent to contracting muscles than in SCAT adjacent to resting muscles? Ten healthy, overnight-fasted males performed one-legged knee extension exercise at 25% of maximal workload (Wmax) for 30 minutes followed by exercise at 55% Wmax for 120 minutes with the other leg and finally exercised at 85% Wmax for 30 minutes with the first leg. Subjects rested for 30 minutes between exercise periods. Femoral SCAT blood flow was estimated from washout of (133)Xe and lipolysis was calculated from femoral SCAT interstitial and arterial glycerol concentrations and blood flow. In general, blood flow as well as lipolysis was higher in femoral SCAT adjacent to contracting than adjacent to resting muscle (time 15-30 min: blood flow: 25% Wmax: 6.6 +/- 1.0 vs. 3.9 +/- 0.8 ml 100 g(-1) min(-1), P < 0.05; 55% Wmax: 7.3 +/- 0.6 vs. 5.0 +/- 0.6, P < 0.05; 85% Wmax: 6.6 +/- 1.3 vs. 5.9 +/- 0.7, P > 0.05; lipolysis: 25% Wmax: 102 +/- 19 vs. 55 +/- 14 nmol 100 g(-1) min(-1), P = 0.06; 55% Wmax: 86 +/- 11 vs. 50 +/- 20, P > 0.05; 85% Wmax: 88 +/- 31 vs. -9 +/- 25, P < 0.05). In conclusion, blood flow and lipolysis are generally higher in SCAT adjacent to contracting than adjacent to resting muscle irrespective of exercise intensity. Thus, specific exercises can induce “spot lipolysis” in adipose tissue. Key words: exercise, spot lipolysis, microdialysis.

Background

The idea of spot reduction is one that has floated around the fitness body recomposition world for decades.   Men want the ever desirable six-pack and can be seen doing abs until the cows come home, women try to slim hips and thighs with endless reps on the inner/outer thigh machine.

Hour long ‘abs’ or ‘buns/thighs’ classes filled with nearly an hour of high rep movements for the specific area can be found in most commercial gyms.  Even in the bodybuilding world, where people really should know better, some still argue that spot reduction can occur and that working a given muscle group will help reduce fat in that specific area.   I addressed this topic somewhat in The Stubborn Fat Solution since some of what I discuss in that book could readily be confused with spot reduction (it’s not).

For the most part, the idea of spot reduction has been resoundly denied by folks in the field (with the occasional heretic or book seller suggesting it is still possible). Various lines of research are usually cited including those showing no difference in skinfolds in the arms of tennis players (who typically use one arm more than the other).

An example I’ve often used is that “If spot reduction worked, people who ate a lot should have skinny faces.” A bit silly but I think it gets the point across.  If working a specific muscle group reduced fat only in that area, that’s how it should work. But it doesn’t.  Or certainly doesn’t seem to.  But, for the most part, the idea hasn’t been directly tested to my knowledge.

In that context, I should note for the sake of background that there are three primary steps involved in fat loss that might potentially be influenced although today’s study only focuses on two.  Those steps are

1. Lipolysis (the actual fat breakdown)
2. Blood flow (critical for transport of the broken down fat to other tissues for ‘burning’)
3. Oxidation (the actual ‘burning’ of fat in tissues such as the liver or skeletal muscle)

Is it possible that performing local activity can impact on some aspect of the above in a way that might make spot reduction or performing endless reps of local exercises worthwhile in terms of fat loss?  That’s what today’s study set out to examine: do contractions in a specific muscle impact on either lipolysis or blood flow (oxidation was not measured) in the adjacent fat cells.

And although it was published several years ago, it still seems to be making the rounds (being cited as ‘evidence’ for spot reduction); as well, the idea of spot reduction is one that refuses to die.  So it’s worth seeing what the real or potentially real effects actually are.

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The Study

Using a couple of different methods (that I’m not going to detail) to measure actual blood flow and lipolysis , the study had subjects perform lower body exercise (they called it one leg leg extension but this probably means one legged cycling) at various intensities while resting the other leg.  That way, blood flow/lipolysis could be measured for the exercise versus the unexercised leg.

This allowed them to compare lipolysis and blood flow in response to local exercise to the non-exercised control leg.  This is actually critically important as any type of whole body exercise would tend to have systemic effects; that is impacting on fuel metabolism all over the body.  By limiting exercise to a single leg, the researchers were able to measure the response only in the fat cells close to the muscles being worked and compare that to the unworked msuscle to see what differences occurred.

Exercise was performed at 25%, 55% and 85% of maximum power output with a 30 minute break and the subjects switched legs from one intensity to the next.  This also acted as a control so that the previous bout of exercise wasn’t impacting on the next bout, since the previously exercised leg got the longer break.  As mentioned above, blood flow and lipolysis was compared between the exercised leg and the rested leg to see what difference the exercise had.

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Results

And, as indicated in the abstract above, both lipolysis and blood flow were increased for the exercised vs. non-exercised leg although this only occurred at the two lower intensities of exercise.    At the highest intensity of exercise, no change was seen.

Before getting to specific numbers, a question worth addressing is why this would have happened.  The researchers proposed two possible reasons for their observation.

First, local changes in hormones (or a synergy between changes in hormones and blood flow) are most likely responsible but there is a larger question of why this would occur in the first place, a point that the researchers specifically made.  By why I mean why the system would work that way in terms of improving physiological functioning.

The reason for asking this question is this: fat mobilized from a specific area of body fat (say the thigh) can’t actually be used for fuel by the muscle underneath it (e.g. the quadriceps).  The blood flow of skeletal muscle and fat cells are separate and any fat mobilized from an adjacent area will go into local circulation; again, it can’t be used directly by that muscle.

So there’s no really logical physiological reason that working a given muscle would would cause fatty acids to be mobilized; that muscle can’t use them.  Of course, physiology doesn’t have to be logical to work a certain way and worrying about the reasons why instead of the observation of what happened can make you lose the forest for the trees.

Related to this, the researchers point out clearly that there is no indication that these results will actually result in spot reduction as fat stores in the affected areas could simply be replenished after the exercise bout.  They didn’t measure fat storage after the exercise bout stopped and process that occurs quite often is fatty acid re-esterification, basically mobilized fat that isn’t burned off elsewhere in the body simply gets stored back in the fat cell.  In some exceedingly strange cases, fat mobilized in one area of the body can be restored in fat cells somewhere else.

The researchers also suggest that localized increase in temperature, which can also impact on blood flow may have also been involved in the measured response.  I discuss this aspect of fat cell mobilization in The Stubborn Fat Solution as local temperature is known to impact on blood flow in the area.  Cold tends to cause vasoconstriction and heat vasodilation so there might actually be some logic to those rubber belts and such that warm the area before exercise.

In any case, for whatever reasons, through whatever mechanism, working a given muscle for 30 minutes at low to moderate intensities did increase fat cell lipolysis in blood flow.

Aha!  Spot reduction is possible, right?  Hang on.

Although clearly local exercise did impact on fat cell lipolysis and blood flow, you might note something I left out of the above discussion: the acutal quantitative impact of this.  That is, how much extra fat was actually mobilized for fuel, potentially to be burned off.

Addressing that very thing, based on the measured changes in blood flow and lipolysis, the researchers estimate that, in 30 minutes of local exercise, an additional .6-2.1 milligrams (one milligram is one thousandth of a gram) per 100 grams of adipose tissue adjacent to contracting muscle was mobilized.

Let me put that in context.   First let’s assume that you’re carying a whopping 5 kg (11.1 pounds) of fat in a specific area.

If local exercise can mobilize 0.6-2.1 milligrams of fat per 100 grams of fat mass, that works out to:

0.6-2.1 mg/100 grams * 1000 grams/kg * 5 kg = 30-105 milligrams of fat.

Or 0.03-0.1 gram of extra fat mobilized in 30 minutes of activity.

Now, a single pound of fat (0.454 kg) contains about 400 grams of fat so our hypothetical 11.1 pounds of fat contains 4,440 grams of fat.  And 30 minutes of local activity mobilized at most 0.1 gram of fat.  Whoo hoo.  You’ll be ripped in about 1000 years.

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Summing Up

And, so far as I’m concerned, that should be the death knell for the idea of spot reduction.  Yes, there appears to be an effect whereby working a given muscle impacts on local fat cell metabolism but the effect is completely and utterly irrelevant in quantatitive terms.  The amount of fat mobilized due to increased hormones or blood flow is simply insignificant to anything in the real world.

There is also the fact that, compared to something like full body cardio types of activities, local single muscle group activities burn tiny amounts of calories.  Doing cardio for 30 minutes at even a reasonable caloric burn of 5 cal/minute (very easy) burns 150 calories.  If you get say 90% fat utilization for fuel, you’ve burned 15 grams of fat.  Compared to the 0.1 gram you might mobilize doing crunches or leg lifts.

As well, the whole body activity will impact on fuel utilization and hormones in ways that much more massively impact on lipolysis and blood flow.  Simply, spending an hour doing localized exercise pales in comparison to the fat loss effects of even moderate cardio.  Wasting time with ab or buns/thighs classes is simply a waste of time in terms of any sort of local fat reduction.