Wednesday, March 28, 2012

"Hashtag Activism"- Is It Working For Public Health?

Last year on Facebook, my timeline was suddenly filled with status updates of just one word.  Red. Black. White.  "What is this?" I thought to myself.  It turned out that it was a breast cancer awareness campaign.  Facebook users were listing their bra colors to help prevent breast cancer.  And there are tons of these public health campaigns all over social networking sites.  "Like" our Facebook page to prevent cancer!  "Re tweet" this message to prevent heart disease!  For those of you familiar with my blog, you'll remember that I think "raising awareness" is the most counter-productive phrase used in our work.  It is not specific enough to measure for change and (on its own) it will not change health behaviors.  

So I thought of my frustration with these campaigns as I read a great article in the New York Times this week called, "Hashtag Activism, and Its Limits".  David Carr writes eloquently about the ease of supporting a variety of causes:

"If you “like” something, does that mean you care about it?  It’s an important distinction in an age when you can accumulate social currency on Facebook or Twitter just by hitting the “like” or “favorite” button.

The ongoing referendum on the Web often seems more like a kind of collective digital graffiti than a measure of engagement: I saw this thing, it spoke to me for at least one second, and here is my mark to prove it".

I like that David brings up the question of engagement here.  Many of these public health campaigns on social media just strive for "likes" on Facebook or "hits" on a website or "re tweets" on Twitter.  And not that they mean nothing, but those measures are just the tip of the iceberg in measuring audience engagement.  And audience engagement (beyond "raising awareness") is what could actually lead to public health activism, knowledge change, and ultimately behavior change. Leslie Lewis gives a great overview of Return on Engagement (ROE) on her blog "digital.good".  According to Leslie, ROE measures tend to be more qualitative and measure message reach and spread.  In addition to "likes", ROE also measures things like brand/campaign awareness, comments, shares, and returning visitors. 

I certainly do not think that all public health campaigns delivered via social media are ineffective.  On the contrary, I think that social media is an incredibly powerful tool for public health.  David Carr makes similar comments later in his article.  Challenging his initial skepticism of web activism, he lists several recent "campaigns" that have been quite effective (e.g., the reversal of Susan G. Komen de-funding Planned Parenthood).

However, to use social media effectively in public health, we must be strategic and we must evaluate.  

Some sample questions that I ask program planners:
  • What is the goal of the campaign?  (e.g., to drive traffic to your website; increase hotline calls; increase specific behaviors like breast self examination?).
  • How will the campaign activities (logically) lead to the desired goal/outcome?
  • Are your goals/outcomes measurable?
  • Have you thought about evaluation before launching the campaign?
  • Besides the ideas listed above, how are you measuring "audience engagement"?
 
 What other questions should we be asking?

Wednesday, March 21, 2012

The Vaccine War: Public Health vs. The Media

A few weeks ago, I had the pleasure of speaking with Laurie Edwards, a writer and blogger who examines chronic illness, healthcare, life balance, public health history, and everything in between.  Among other topics, we talked about the role of social media and mainstream media in the vaccine debate.

So I thought of that conversation as I watched last night's re-run of PBS Frontline's special "The Vaccine War" (originally aired April 27, 2010).  I highly encourage advocates on both sides of the issue to check it out.

The piece was quite balanced with interviews on all sides.  For example, we heard from vaccine advocate Dr. Paul Offit, bioethicist Dr. Arthur Caplan, anti-vaccine advocate parents in Ashland, OR (with one of the lowest vaccine rates in the country),  Jenny McCarthy and her colleagues at Generation Rescue who continue to argue for evidence of the link between vaccines and autism, and from parents of a girl who almost died at 6 weeks from whooping cough.

It explored possible contributors to the fear of vaccines and/or the lowering vaccination rates:
  • 1998 Lancet article by Dr. Andrew Wakefield that linked autism to childhood vaccines (*This article has since been retracted and MANY U.S. and International epidemiological studies have found no scientific evidence of a causal link)
  • A new generation of parents that are too young to know the devastating effects of vaccine-preventable diseases like polio.  One interviewee used a term that I really like- "Community Recollection".  As Community Recollection of these diseases disappears, we can become complacent.
  • A false sense of security because many of these diseases are not seen frequently in the United States.  However, we forget that with the ease of air travel, borders are almost non-existent.  For example, the piece followed an outbreak of measles in San Diego that started when a non-vaccinated 7 year old from the US contracted it while vacationing in Switzerland and brought it home to classmates.
  • The Internet.  While it also offers many positive benefits regarding healthcare (e.g., access to information/publications; online support groups and connections with a "community" of individuals with similar diagnoses)- it also has its potential downfalls.
    • It can keep controversy alive- even after it has been disproven (e.g., the Wakefield article)
    • False or unproven information can go viral and it is hard to retract!  They use the example of the youtube video of Desiree Jennings (a 25 year-old Washington Redskins cheerleader) that claimed that a flu shot caused her debilitating muscle disorder.  
So how can Public Health compete with the Media and the Internet?
  • Let's not compete.  Let's collaborate.  Let's learn (either through our own capacity or collaboration) to effectively communicate public health information online.  Our biggest audience (the public) is not usually reading our peer reviewed journals or attending our annual conferences.  This is already starting to happen.  Public health organizations have active Facebook and twitter accounts, blogs, videos.  Let's keep going.  And let's train our public health colleagues/students in health communication.
  • Let's remember to share the spotlight with celebrities and other spokespeople that have influence over the public.  Like it or not, the way people get their health information and make decisions is changing.  They do not just agree with doctors or scientists.  I almost always see these debates featuring Public Health (scientist speaking in jargon) vs. Celebrity/Parent with moving emotional story about their child being injured by a vaccine.  That is hard to compete with!  Believe it or not (because we seem to only hear from Jenny McCarthy), there are also pro-vaccine celebrities.  Jennifer Garner and Kristi Yamaguchi have been flu vaccine advocates.  Jennifer Lopez and Keri Russell have been pertussis vaccine advocates.  Let's make sure the public knows that. 
  • Let's think about the framing and marketing of vaccination messages.  When the HPV vaccine is framed as a Cancer Vaccine for both boys and girls...versus an STD Vaccine for just girls/young women it is perceived very differently by the public.
Tell me what you think:
  • What can we do to change the current "Community Recollection" about vaccinations?
  • Other suggestions regarding how public health can collaborate with the media/internet sites to communicate more effectively with the public?

Monday, March 19, 2012

The 2012 red meat-mortality study (Arch Intern Med): The data suggests that red meat is protective

I am not a big fan of using arguments such as “food questionnaires are unreliable” and “observational studies are worthless” to completely dismiss a study. There are many reasons for this. One of them is that, when people misreport certain diet and lifestyle patterns, but do that consistently (i.e., everybody underreports food intake), the biasing effect on coefficients of association is minor. Measurement errors may remain for this or other reasons, but regression methods (linear and nonlinear) assume the existence of such errors, and are designed to yield robust coefficients in their presence. Besides, for me to use these types of arguments would be hypocritical, since I myself have done several analyses on the China Study data (), and built what I think are valid arguments based on those analyses.

My approach is: Let us look at the data, any data, carefully, using appropriate analysis tools, and see what it tells us; maybe we will find evidence of measurement errors distorting the results and leading to mistaken conclusions, or maybe not. With this in mind, let us take a look at the top part of Table 3 of the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality, authored by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (). This is a prominent journal, with an average of over 270 citations per article according to Google Scholar. The study has received much media attention recently.


Take a look at the area highlighted in red, focusing on data from the Health Professionals sample. That is the multivariate-adjusted cardiovascular mortality rate, listed as a normalized percentage, in the highest quintile (Q5) of red meat consumption from the Health Professionals sample. The non-adjusted percentages are 1.4  percent mortality in Q5 and 1.13 in Q1 (from Table 1 of the same article); so the multivariate adjustment-normalization changed the values of the percentages somewhat, but not much. The highlighted 1.35 number suggests that for each group of 100 people who consumed a lot of red meat (Q5), when compared with a group of 100 people who consumed little red meat (Q1), there were on average 0.35  more deaths over the same period of time (more than 20 years).

The heavy red meat eaters in Q5 consumed 972.37 percent more red meat than those in Q1. This is calculated with data from Table 1 of the same article, as: (2.36-0.22)/0.22. In Q5, the 2.36 number refers to the number of servings of red meat per day, with each serving being approximately 84 g. So the heavy red meat eaters ate approximately 198 g per day (a bit less than 0.5 lb), while the light red meat eaters ate about 18 g per day. In other words, the heavy red meat eaters ate 9.7237 times more, or 972.37 percent more, red meat.

So, just to be clear, even though the folks in Q5 consumed 972.37 percent more red meat than the folks in Q1, in each matched group of 100 you would not find a single additional death over the same time period. If you looked at matched groups of 1,000 individuals, you would find 3 more deaths among the heavy red meat eaters. The same general pattern, of a minute difference, repeats itself throughout Table 3. As you can see, all of the reported mortality ratios are 1-point-something. In fact, this same pattern repeats itself in all mortality tables (all-cause, cardiovascular, cancer). This is all based on a multivariate analysis that according to the authors controlled for a large number of variables, including baseline history of diabetes.

Interestingly, looking at data from the same sample (Health Professionals), the incidence of diabetes is 75 percent higher in Q5 than in Q1. The same is true for the second sample (Nurses Health), where the Q5-Q1 difference in incidence of diabetes is even greater - 81 percent. This caught my eye, being diabetes such a prototypical “disease of affluence”. So I entered the whole data reported in the article into HCE () and WarpPLS (), and conducted some analyses. The graphs below are from HCE. The data includes both samples – Health Professionals and Nurses Health.




HCE calculates bivariate correlations, and so does WarpPLS. But WarpPLS stores numbers with a higher level of precision, so I used WarpPLS for calculating coefficients of association, including correlations. I also double-checked the numbers with other software, just in case (e.g., SPSS and MATLAB). Here are the correlations calculated by WarpPLS, which refer to the graphs above: 0.030 for red meat intake and mortality; 0.607 for diabetes and mortality; and 0.910 for food intake and diabetes. Yes, you read it right, the correlation between red meat intake and mortality is a very low and non-significant 0.030 in this dataset. Not a big surprise when you look at the related HCE graph, with the line going up and down almost at random. Note that I included the quintiles data from both the Health Professionals and Nurses Health samples in one dataset.

Those folks in Q5 had a much higher incidence of diabetes, and yet the increase in mortality for them was significantly lower, in percentage terms. A key difference between Q5 and Q1 being what? The Q5 folks ate a lot more red meat. This looks suspiciously suggestive of a finding that I came across before, based on an analysis of the China Study II data (). The finding was that animal food consumption (and red meat is an animal food) was protective, actually reducing the negative effect of wheat flour consumption on mortality. That analysis actually suggested that wheat flour consumption may not be so bad if you eat 221 g or more of animal food daily.

So, I built the model below in WarpPLS, where red meat intake (RedMeat) is hypothesized to moderate the relationship between diabetes incidence (Diabetes) and mortality (Mort). Below I am also including the graphs for the direct and moderating effects; the data is standardized, which reduces estimation error, particularly in moderating effects estimation. I used a standard linear algorithm for the calculation of the path coefficients (betas next to the arrows) and jackknifing for the calculation of the P values (confidence = 1 – P value). Jackknifing is a resampling technique that does not require multivariate normality and that tends to work well with small samples; as is the case with nonparametric techniques in general.




The direct effect of diabetes on mortality is positive (0.68) and almost statistically significant at the P < 0.05 level (confidence of 94 percent), which is noteworthy because the sample size here is so small – only 10 data points, 5 quintiles from the Health Professionals sample and 5 from the Nurses Health sample. The moderating effect is negative (-0.11), but not statistically significant (confidence of 61 percent). In the moderating effect graphs (shown side-by-side), this negative moderation is indicated by a slightly less steep inclination of the regression line for the graph on the right, which refers to high red meat intake. A less steep inclination means a less strong relationship between diabetes and mortality – among the folks who ate the most red meat.

Not too surprisingly, at least to me, the results above suggest that red meat per se may well be protective. Although we should consider a least two other possibilities. One is that red meat intake is a marker for consumption of some other things, possibly present in animal foods, that are protective - e.g., choline and vitamin K2. The other possibility is that red meat is protective in part by displacing other less healthy foods. Perhaps what we are seeing here is a combination of these.

Whatever the reason may be, red meat consumption seems to actually lessen the effect of diabetes on mortality in this sample. That is, according to this data, the more red meat is consumed, the fewer people die from diabetes. The protective effect might have been stronger if the participants had eaten more red meat, or more animal foods containing the protective factors; recall that the threshold for protection in the China Study II data was consumption of 221 g or more of animal food daily (). Having said that, it is also important to note that, if you eat excess calories to the point of becoming obese, from red meat or any other sources, your risk of developing diabetes will go up – as the earlier HCE graph relating food intake and diabetes implies.

Please keep in mind that this post is the result of a quick analysis of secondary data reported in a journal article, and its conclusions may be wrong, even though I did my best not to make any mistake (e.g., mistyping data from the article). The authors likely spent months, if not more, in their study; and have the support of one of the premier research universities in the world. Still, this post raises serious questions. I say this respectfully, as the authors did seem to try their best to control for all possible confounders.

I should also say that the moderating effect I uncovered is admittedly a fairly weak effect on this small sample and not statistically significant. But its magnitude is apparently greater than the reported effects of red meat on mortality, which are not only minute but may well be statistical artifacts. The Cox proportional hazards analysis employed in the study, which is commonly used in epidemiology, is nothing more than a sophisticated ANCOVA; it is a semi-parametric version of a special case of the broader analysis method automated by WarpPLS.

Finally, I could not control for confounders because, given the small sample, inclusion of confounders (e.g., smoking) leads to massive collinearity. WarpPLS calculates collinearity estimates automatically, and is particularly thorough at doing that (calculating them at multiple levels), so there is no way to ignore them. Collinearity can severely distort results, as pointed out in a YouTube video on WarpPLS (). Collinearity can even lead to changes in the signs of coefficients of association, in the context of multivariate analyses - e.g., a positive association appears to be negative. The authors have the original data – a much, much larger sample - which makes it much easier to deal with collinearity.

Moderating effects analyses () – we need more of that in epidemiological research eh?

Monday, March 12, 2012

Gaining muscle and losing fat at the same time: A more customized approach based on strength training and calorie intake variation

In the two last posts I discussed the idea of gaining muscle and losing fat at the same time () (). This post outlines one approach to make that happen, based on my own experience and that of several HCE () users. This approach may well be the most natural from an evolutionary perspective.

But first let us address one important question: Why would anyone want to reach a certain body weight and keep it constant, resorting to the more difficult and slow strategy of “turning fat into muscle”, so to speak? One could simply keep on losing fat, without losing or gaining muscle, until he or she reaches a very low body fat percentage (e.g., a single-digit body fat percentage, for men). Then he or she could go up from there, slowly putting on muscle.

The reason why it is advisable to reach a certain body weight and keep it constant is that, below a certain weight, one is likely to run into nutrient deficiencies. Non-exercise energy expenditure is proportional to body weight. As you keep on losing body weight, calorie intake may become too low to allow you to have a nutrient intake that is the minimum for your body structure. Unfortunately eating highly nutritious vegetables or consuming copious amounts of vitamin and mineral supplements will not work very well, because the nutritional needs of your body include both micro- and macro-nutrients that need co-factors to be properly absorbed and/or metabolized. One example is dietary fat, which is necessary for the absorption of fat-soluble vitamins.

If you place yourself into a state of nutrient deficiency, your body will compensate by mounting a multipronged defense, resorting to psychological and physiological mechanisms. Your body will do that because it is hardwired for self-preservation; as noted below, being in a state of nutrient deficiency for too long is very dangerous for one's health. Most people cannot oppose this body reaction by willpower alone. That is where binge-eating often starts. This is one of the key reasons why looking for a common denominator of most diets leads to the conclusion that all succeed at first, and eventually fail ().

If you are one of the few who can oppose the body’s reaction, and maintain a very low calorie intake even in the face of nutrient deficiencies, chances are you will become much more vulnerable to diseases caused by pathogens. Individually you will be placing yourself in a state that is similar to that of populations that have faced famine in the past. Historically speaking, famines are associated with decreases in degenerative diseases, and increases in diseases caused by pathogens. Pandemics, like the Black Death (), have historically been preceded by periods of food scarcity.

The approach to gaining muscle and losing fat at the same time, outlined here, relies mainly on the following elements: (a) regularly conducting strength training; (b) varying calorie intake based on exercise; and (c) eating protein regularly. To that, I would add becoming more active, which does not necessarily mean exercising but does mean doing things that involve physical motion of some kind (e.g., walking, climbing stairs, moving things around), to the tune of 1 hour or more every day. These increase calorie expenditure, enabling a slightly higher calorie intake while maintaining the same weight, and thus more nutrients on a diet of unprocessed foods. In fact, even things like fidgeting count (). These activities should not cause muscle damage to the point of preventing recovery from strength training.

As far as strength training goes, the main idea, as discussed in the previous post, is to regularly hit the supercompensation window, with progressive overload, and maintain your current body weight. In fact, over time, as muscle gain progresses, you will probably want to increase your calorie intake to increase your body weight, but very slowly to keep any fat gain from happening. This way your body fat percentage will go down, even as your weight goes up slowly. The first element, regularly hitting the supercompensation window, was discussed in a previous post ().

Varying calorie intake based on exercise. Here one approach that seems to work well is to eat more in the hours after a strength training session, and less in the hours preceding the next strength training session, keeping the calorie intake at maintenance over a week. Individual customization here is very important. Many people will respond quite well to a calorie surplus window of 8 – 24 h after exercise, and a calorie deficit in the following 40 – 24 h. This assumes that strength training sessions take place every other day. The weekend break in routine is a good one, as well as other random variations (e.g., random fasts), as the body tends to adapt to anything over time ().

One example would be someone following a two-day cycle where on the first day he or she would do strength training, and eat the following to satisfaction: muscle meats, fatty seafood (e.g., salmon), cheese, eggs, fruits, and starchy tubers (e.g., sweet potato). On the second day, a rest day, the person would eat the following, to near satisfaction, limiting portions a bit to offset the calorie surplus of the previous day: organ meats (e.g., heart and liver), lean seafood (e.g., shrimp and mussels), and non-starchy nutritious vegetables (e.g., spinach and cabbage). This would lead to periodic glycogen depletion, and also to unsettling water-weight variations; these can softened a bit, if they are bothering, by adding a small amount of fruit and/or starchy foods on rest days.

Organ meats, lean seafood, and non-starchy nutritious vegetables are all low-calorie foods. So restricting calories with them is relatively easy, without the need to reduce the volume of food eaten that much. If maintenance is achieved at around 2,000 calories per day, a possible calorie intake pattern would be 3,000 calories on one day, mostly after strength training, and 1,000 calories the next. This of course would depend on a number of factors including body size and nonexercise thermogenesis. A few calories could be added or removed here and there to make up for a different calorie intake during the weekend.

Some people believe that, if you vary your calorie intake in this way, the calorie deficit period will lead to muscle loss. This is the rationale behind the multiple balanced meals a day approach; which also works, and is successfully used by many bodybuilders, such as Doug Miller () and Scooby (). However, it seems that the positive nitrogen balance stimulus caused by strength training leads to a variation in nitrogen balance that is nonlinear and also different from the stimulus to muscle gain. Being in positive or neutral nitrogen balance is not the same as gaining muscle mass, although the two should be very highly correlated. While the muscle gain window may close relatively quickly after the strength training session, the window in which nitrogen balance is positive or neutral may remain open for much longer, even in the face of a calorie deficit during part of it. This difference in nonlinear response is illustrated through the schematic graph below.


Eating protein regularly. Here what seems to be the most advisable approach is to eat protein throughout, in amounts that make you feel good. (Yes, you should rely on sense of well being as a measure as well.) There is no need for overconsumption of protein, as one does not need much to be in nitrogen balance when doing strength training. For someone weighing 200 lbs (91 kg) about 109 g/d of high-quality protein would be an overestimation () because strength training itself pushes one’s nitrogen balance into positive territory (). The amount of carbohydrate needed depends on the amount of glycogen depleted through exercise and the amount of protein consumed. The two chief sources for glycogen replenishment, in muscle and liver, are protein and carbohydrate – with the latter being much more efficient if you are not insulin resistant.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder (). Still, consumption of protein stimulates muscle growth through complex processes. And protein does not usually become fat if one is in calorie deficit, particularly if consumption of carbohydrates is limited ().

The above is probably much easier to understand than to implement in practice, because it requires a lot of customization. It seems natural because our Paleolithic ancestors probably consumed more calories after hunting-gathering activities (i.e., exercise), and fewer calories before those activities. Our body seems to respond quite well to alternate day calorie restriction (). Moreover, the break in routine every other day, and the delayed but certain satisfaction provided by the higher calorie intake on exercise days, can serve as powerful motivators.

The temptation to set rigid rules, or a generic formula, always exists. But each person is unique (). For some people, adopting various windows of fasting (usually in the 8 – 24 h range) seems to be a very good strategy to achieve calorie deficits while maintaining a positive or neutral nitrogen balance.

For others, fasting has the opposite effect, perhaps due to an abnormal increase in cortisol levels. This is particularly true for fasting windows of 12 – 24 h or more. If regularly fasting within this range stresses you out, as opposed to “liberating” you (), you may be in the category that does better with more frequently meals.

Wednesday, March 7, 2012

Tumblr Contemplates A New Policy Against Self-Harm Blogs: Let's All Weigh In

Tumblr is a popular microblogging platform.  It lets you share anything from text to pictures to video.  According to their website, the average Tumblr user creates 14 original posts each month, and reblogs 3. The "reblog" button on all Tumblr posts allows a meme to spread rapidly across thousands of blogs with just one click.

As with other social media platforms, tumblr has an enormous reach (18,878,347,183 total posts as of the time of this blog).  Therefore, it has great potential to help and hurt the public's health as it facilitates communication among millions of people.

A few weeks ago, Tumblr presented to its users a challenge (and possible solution) regarding blogs that promote self-harm.  Their users are being asked to weigh in on the policy.  I think that is a smart move.

Here is an excerpt from the Tumblr staff blog

Our Content Policy has not, until now, prohibited blogs that actively promote self-harm. These typically take the form of blogs that glorify or promote anorexia, bulimia, and other eating disorders; self-mutilation; or suicide. These are messages and points of view that we strongly oppose, and don’t want to be hosting. The question for us has been whether it’s better to (a) prohibit them, as a statement against the very ideas of self-harm that they are advancing, or (b) permit them to stay up, accompanied by a public service warning that directs readers to helplines run by organizations like the National Eating Disorders Association.

We are planning to post a new, revised Content Policy in the very near future, and we’d like to ask for input from the Tumblr community on this issue.

The blog goes on to say that they currently think the right answer is to implement a policy against pro self-harm blogs.  They aim to focus only on blogs that actively glorify or promote these behaviors. They also intend to start showing public service announcements (PSAs) on specific search terms like "anorexic" or "thinsperation".  It is unclear from their post how this policy will actually be implemented.  It would take enormous staff resources to comprehensively review their site and remove concerning materials. 

Other online and social media platforms have struggled with similar issues regarding how to respond to users that may be searching for or posting worrisome content.  Here are a few examples of other challenges and solutions:
From these examples, you can see that there have been a variety of approaches to address potentially unhealthy or unsafe posts on social media platforms.  Sites can decide to be inclusive of all posts, they can let users police each other and report concerns, they can post resources in response to keywords, they can actively prohibit certain content...or they can use some combination of these strategies.
  • What strategy or combination of strategies is best for the public's health?  
  • If users are prohibited from posting, does that make them more isolated and less likely to connect to services?
  • Should the overall health of the user group outweigh the health of that individual?
Tumblr is encouraging users to weigh in on their plan...so I ask you to both comment here and contact them at policy@tumblr.com

Monday, March 5, 2012

Gaining muscle and losing fat at the same time: Various issues and two key requirements

In my previous post (), I mentioned that the idea of gaining muscle and losing fat at the same time seems impossible to most people because of three widely held misconceptions: (a) to gain muscle you need a calorie surplus; (b) to lose fat you need a calorie deficit; and (c) you cannot achieve a calorie surplus and deficit at the same time.

The scenario used to illustrate what I see as a non-traumatic move from obese or seriously overweight to lean is one in which weight loss and fat loss go hand in hand until a relatively lean level is reached, beyond which weight is maintained constant (as illustrated in the schematic graph below). If you are departing from an obese or seriously overweight level, it may be advisable to lose weight until you reach a body fat level of around 21-24 percent for women or 14-17 percent for men. Once you reach that level, it may be best to stop losing weight, and instead slowly gain muscle and lose fat, in equal amounts. I will discuss the rationale for this in more detail in my next post; this post will focus on addressing the misconceptions above.


Before I address the misconceptions, let me first clarify that, when I say “gaining muscle” I do not mean only increasing the amount of protein stored in muscle tissue. Muscle tissue is mostly water, by far. An important component of muscle tissue is muscle glycogen, which increases dramatically with strength training, and also tends to increase the amount of water stored in muscle. So, when you gain muscle, you gain a significant amount of water.

Now let us take a look at the misconceptions. The first misconception, that to gain muscle you need a calorie surplus, was dispelled in a previous post featuring a study by Ballor and colleagues (). In that study, obese subjects combined strength training with a mild calorie deficit, and gained muscle. They also lost fat, but ended up a bit heavier than at the beginning of the intervention. Another study along the same lines was linked by Clint (thanks) in the comments section under the last post ().

The second misconception, that to lose fat you need a calorie deficit; is related to the third, that you cannot achieve a calorie surplus and deficit at the same time. In part these misconceptions are about semantics, as most people understand “calorie deficit” to mean “constant calorie deficit”. One can easily vary calorie intake every other day, generating various calorie deficits and surpluses over a week, but with no overall calorie deficit or surplus for the entire week. This is why I say that one can achieve a calorie surplus and deficit “at the same time”. But let us make a point very clear, most of the evidence that I have seen so far suggests that you do not need a calorie deficit to lose fat, but you do need a calorie deficit to lose structural weight (i.e., non-water weight). With a few exceptions, not many people will want to lose structural weight by shedding anything other than body fat. One exception would be professional athletes who are already very lean and yet are very big for the weight class in which they compete, being unable to "make weight" through dehydration.

Perhaps the most surprising to some people is that, based on my own experience and that of several HCE () users, you don’t even need to vary your calorie intake that much to gain muscle and lose fat at the same time. You can achieve that by eating enough to maintain your body weight. In fact, you can even slowly increase your calorie intake over time, as muscle growth progresses beyond the body fat lost. And here I mean increasing your calorie intake very slowly, proportionally to the amount of muscle you gain; which also means that the incremental increase in calorie intake will vary from person to person. If you are already relatively lean, at around 21-24 percent of body fat for women and 14-17 percent for men, gaining muscle and losing fat in equal amounts will lead to a visible change in body composition over time () ().

Two key requirements seem to be common denominators for most people. You must eat protein regularly; not because muscle tissue is mostly protein, but because protein seems to act as a hormone, signaling to muscle tissue that it should repair itself. (Many hormones are proteins, actually peptides, and also bind to receptor proteins.) And you also must conduct strength training to the point that you are regularly hitting the supercompensation window (). This takes a lot of individual customization (). You can achieve that with body weight exercises, although free weights and machines seem to be generally more effective. Keep in mind that individual customization will allow you to reach your "sweet spots", but that still results will vary across individuals, in some cases dramatically.

If you regularly hit the supercompensation window, you will be progressively spending slightly more energy in each exercise session, chiefly in the form of muscle glycogen, as you progress with your strength training program. You will also be creating a hormonal mix that will increase the body’s reliance on fat as a source of energy during recovery. As a compensatory adaptation (), your body will gradually increase the size of its glycogen stores, raising insulin sensitivity and making it progressively more difficult for glucose to become body fat.

Since you will be progressively spending slightly more energy over time due to regularly hitting the supercompensation window, that is another reason why you will need to increase your calorie intake. Again, very slowly, proportionally to your muscle gain. If you do not do that, you will provide a strong stimulus for autophagy () to occur, which I think is healthy and would even recommend from time to time. In fact, one of the most powerful stimuli to autophagy is doing strength training and fasting afterwards. If you do that only occasionally (e.g., once every few months), you will probably not experience muscle loss or gain, but you may experience health improvements as a result of autophagy.

The human body is very adaptable, so there are many variations of the general strategy above. In my next post, I will talk a bit more about a variation that seems to work well for many people. It involves a combination of strength training and calorie intake variation that may well be the most natural from an evolutionary perspective.