If you\u2019ve never been dissected \u2013 since you\u2019re reading this article, I assume you haven\u2019t \u2013 you\u2019ve never had your body composition measured<\/em>. You\u2019ve only had your body composition estimated<\/em>. Understanding this fact is important for two reasons. First, it invites a simple question: \u201cwhat IS actually being measured in order to estimate body composition?\u201d Second, it invites us to consider the validity and reliability of body composition estimates.<\/p>\n\n\n\n There are five types of measurements<\/em> commonly used to estimate body composition. I\u2019ll briefly explain each measurement, and the methods of body composition analysis that use each measurement.<\/p>\n\n\n\n Two popular methods of body composition analysis \u2013 underwater weighing and the Bod Pod (air displacement plethysmography) \u2013 estimate body density in order to estimate body composition. Density = Mass \u00f7 Volume. With the BodPod, body volume is estimated based on air pressure changes within an enclosed chamber. With underwater weighing, density is estimated based on the Archimedes principle<\/a>. <\/p>\n\n\n\n These methods of body composition analysis rely on one simple fact: different body tissues have different densities. For example, muscle is about 15% more dense than fat \u2013 it weighs about 15% more per unit of volume. So, if two people weigh the same amount, but the total volume of Person A\u2019s body is smaller than the total volume of Person B\u2019s body, Person A would have a greater density than Person B. Therefore, all else being equal, Person A should have more muscle mass and less body fat than person B.<\/p>\n\n\n\n Your body stores fat in quite a few different places, but the two largest compartments of fat storage are the subcutaneous compartment \u2013 fat stored between your skin and muscles \u2013 and the visceral compartment \u2013 fat stored around the organs in the abdominal cavity.<\/p>\n\n\n\n In general, higher amounts of subcutaneous fat are correlated with higher amounts of total<\/em> body fatness. Estimating body composition using calipers takes advantage of this relationship. You can\u2019t easily measure the amount of fat within your abdominal cavity, but subcutaneous fat is far more accessible. A trained assessor uses calipers to measure the thickness of your subcutaneous fat, in order to estimate the total amount of fat on and in your body. Once these subcutaneous thicknesses are obtained, they\u2019re entered into formulas that lean on population-level observations about the typical relationship between subcutaneous fat storage and total body fat percentage.<\/p>\n\n\n\n A similar but less accessible method of body composition assessment uses ultrasound (in place of calipers) to accomplish the same purpose.<\/p>\n\n\n\n Your body tissues conduct or impede electrical currents to different degrees. In general, tissues with more water are better conductors of an electrical current.<\/p>\n\n\n\n One popular method of estimating body composition takes advantage of this fact: bioelectrical impedance analysis (BIA). Bathroom scales and small handheld devices that estimate your body fat percentage are using BIA.<\/p>\n\n\n\n These devices pass a weak electrical current between electrodes, and measure how long it takes for the electrical current to pass through your body\u2019s tissues. Since muscle has a greater water content than fat tissue (~75% versus ~10%), the electrical current will travel a bit faster through leaner people. In essence, BIA is estimating the hydration level of the tissues an electrical current is passing through, in order to estimate the amount of each type of tissue (fat versus lean) the current is passing through.<\/p>\n\n\n\n Bioelectrical impedance spectroscopy (BIS) is a similar (but strictly superior) method of body composition assessment that relies on this same relationship. BIS uses a wide array of electrical currents across a broad spectrum of frequencies to differentiate between intracellular and extracellular water \u2013 this differentiation allows for more nuanced body composition estimates compared to relying on total body water alone.<\/p>\n\n\n\n There are a few different methods for estimating body composition that rely on measurements of body shape \u2013 typically height and various circumference measurements. The most popular formulae in this category are the US Navy formula<\/a> and waist-to-height ratios<\/a> (though there are certainly others). 3D body scanners are a high-tech variation on the same theme.<\/p>\n\n\n\n In essence, methods of estimating body composition based on body geometry rely on the fact that most humans have characteristic fat storage patterns. People store most of their body fat around their waist and hips. More often than not, if two people are the same height, the person with the larger waist will be carrying more body fat.<\/p>\n\n\n\n Savvy readers have probably noticed that I haven\u2019t mentioned DEXA (dual-energy x-ray absorptiometry) scanners yet. I think people tend to overestimate the accuracy and reliability of DEXA scans for estimating body composition, but DEXA scans are<\/em> probably the best method of estimating body composition that\u2019s somewhat accessible.<\/p>\n\n\n\n The way DEXA scanners estimate body composition is pretty interesting, though \u2013 they use a combination of very high-tech and very simplistic techniques.<\/p>\n\n\n\n A DEXA scanner passes x-rays (with a relatively low radiation dose) through your body, and measures how much of the energy each part of your body absorbs \u2013 bone absorbs the most energy, muscle absorbs less energy, and fat absorbs the least energy. This creates an image of your body where bones are nearly white, muscle tissue is a bit darker, and fat tissue is darker yet. From there, a computer program essentially just counts the number of pixels of different colors. If you have more dark (fat) pixels than semi-dark (muscle) pixels, the scanner will say your body fat percentage is higher (and vice versa). <\/p>\n\n\n\n Due to the relatively expensive and high-tech nature of these scanners, people often assume that their body composition estimates are nearly perfect. However, there are some noteworthy limitations that are rarely acknowledged outside of academic journals. For example, DEXA scanners are unable to directly measure tissue thickness, so they are forced to derive composition estimates of a 3-dimensional body from a 2-dimensional scan. Further, the devices are only able to estimate soft tissue composition (i.e., the relative content of lean tissue and fat tissue) in pixels that contain no bone mass. For most scans, about 40% of the total pixels will contain bone, so nearly half of the scan is essentially uninterpretable (without assumption-driven calculations) for the purpose of body fat estimation.<\/p>\n\n\n\n Since all popular methods of assessing body composition aren\u2019t actually measuring<\/em> body composition \u2013 they\u2019re measuring things that are proxies for body composition \u2013 it\u2019s worth asking a simple question: How good are the estimates produced by these technologies?<\/p>\n\n\n\n The answer to that question largely depends on the reason you\u2019re interested in assessing body composition in the first place.<\/p>\n\n\n\n If you\u2019re a researcher, and you\u2019re interested in characterizing the body composition of a group of subjects, or you\u2019re interested in assessing how a particular training or nutrition intervention affects average<\/em> changes in body composition over time, all of the methods above work pretty well (except for BIA, arguably).<\/p>\n\n\n\n There aren\u2019t a ton of human cadavers lying around (though animal cadavers are sometimes used), so when a new technology for assessing body composition hits the market, it\u2019s usually compared against body composition estimates derived from a 4-compartment model (which is considered the practical<\/em> gold standard for estimating body composition) 1<\/a><\/sup>A full 4-compartment body composition assessment combines various techniques of assessing body composition in order to estimate fat mass, total mineral mass (mostly from bone), total protein mass, and total body water. You generally won\u2019t find all of the necessary equipment to do a 4-compartment body composition assessment outside of a research laboratory. As such, while full 4-compartment body composition assessments are quite accurate, they\u2019re rarely accessible for individuals interested in monitoring their body composition<\/span>. Most methods of body composition analysis produce group-level estimates of lean mass and body fat percentage that differ from the gold standard analytic method by ~1-4%, on average. Furthermore, estimated changes<\/em> in body composition tend to be pretty similar when comparing the aforementioned technologies to estimates derived from a 4-compartment model \u2013 if a 4-compartment model says a group of subjects decreased their body fat percentage by an average of 5%, the technologies above will generally estimate that body fat percentage decreased by ~3-7%.<\/p>\n\n\n\n So, in general, if you\u2019re interested in assessing average<\/em> body composition for a group of people, or tracking average<\/em> changes over time, most popular methods of body composition analysis work pretty well. Some certainly work better than others \u2013 if DEXA and BIA are both available to you, I can\u2019t think of a good reason to choose BIA over DEXA \u2013 but they all typically do a good-to-excellent job of estimating the body composition of groups<\/em> of people.<\/p>\n\n\n\n However, I suspect that most people reading this article aren\u2019t particularly interested in assessing the average body composition of a group of 50 subjects. I suspect you\u2019re interested in estimating your<\/em> body composition, and tracking your<\/em> body composition over time.<\/p>\n\n\n\n If that\u2019s the case, I\u2019ve got some bad news for you: There\u2019s really not a particularly good method for estimating body composition (assuming you\u2019re interested in having accurate, precise data).<\/p>\n\n\n\n For this point, I\u2019ll refer you to an excellent series of articles by James Krieger, if you\u2019d like to do a deep dive into this topic (one<\/a>, two<\/a>, three<\/a>, four<\/a>, five<\/a>, six<\/a>, seven<\/a>). However, the basic point is pretty straightforward: You shouldn\u2019t assume that group-level<\/em> errors generalize to individuals.<\/p>\n\n\n\n A method of assessing body composition could underestimate body fat percentage by 1% on average, while still producing massive underestimates and<\/em> overestimates for individuals. To illustrate, an underestimate of 11% and an overestimate of 9% equate to an average<\/em> underestimate of just 1%.<\/p>\n\n\n\n The figures below demonstrate this point with illustrative synthetic data. The scatterplot shows the relationship between actual body fat percentage on the x-axis, and estimated body fat percentage on the y-axis. Overall, the estimated body fat percentage data looks quite good \u2013 estimated values are highly correlated with actual values (r = 0.89), and the summary statistics bear this out. The average error is only 0.5% (25.3% vs. 25.8%), and the average absolute error is just 2.7% (which is similar to DEXA). However, the histogram tells a slightly different story. Less than half of the individual errors were smaller than 3%, around a third of the errors were between 3% and 6%, and about 1\/6th of the errors were larger than 6%, topping out at 10.1%.<\/p>\n\n\n\n For both estimating body fat percentage at a single point in time, and for estimating changes in body fat percentage over time, the methods of body composition analysis listed above (even DEXA) can produce individual<\/em> errors of up to ~4-5% at best, and errors exceeding 10% at worst.<\/p>\n\n\n\n In other words, if your body fat percentage is estimated to be 20%, that actually<\/em> means your body fat percentage is somewhere between 15-25% if you feel particularly optimistic about body composition estimation; more realistically, it means your body fat percentage is somewhere between 10-30%. To say that such a wide range is minimally informative would be an understatement.<\/p>\n\n\n\n Similarly, if you track your body composition over time, and a particular method for assessing body composition suggests that your body fat percentage has decreased by 5%…that optimistically means it has decreased by 0-10%, and more pessimistically means your body fat percentage has either increased by up to 5%, or decreased by up to 15%. I suspect that virtually everyone would be able to reach the same conclusion (probably with an even higher degree of precision) by just looking in the mirror or assessing how their clothes fit.<\/p>\n\n\n\n In short, assessing body composition can be very valuable in a research context. If we want to see how a particular intervention affects changes in fat or lean mass for a group of subjects, we have plenty of tools that are well-suited to the job. However, I\u2019d argue that assessing body composition offers virtually no utility whatsoever for individuals. On the group level, we can estimate body composition at a single point in time quite well, and track changes in body composition over time quite accurately. On the individual level, estimates of body composition at a single point in time are too imprecise to be particularly informative, and estimated changes over time are too imprecise to be particularly useful.<\/p>\n\n\n\n As one final illustration, let\u2019s assume that you recently completed a diet, and your body mass decreased from 100kg to 80kg. You got DEXA scans before and after your diet, and DEXA said you went from 30% body fat to 20% body fat.<\/p>\n\n\n\n If you take those numbers at face value, you\u2019d assume that you lost 14kg of fat mass and 6kg of lean mass. Depending on your perspective, you may consider that to be a pretty successful diet (your body fat percentage decreased by 10%), or you may consider it to be a failure, especially if you engage in resistance training (6kg is a lot of lean mass to lose). However, if we optimistically<\/em> assume that DEXA produces individual errors of up to \u201cjust\u201d 5% for tracking changes in body composition over time, this diet could tell two very different stories.<\/p>\n\n\n\n For instance, if your body fat percentage decreased by 15% instead of 10%, now the diet looks like an unqualified success. You lost 18kg of fat and just 2kg of lean mass \u2013 I think virtually anyone would be stoked about that outcome. However, if your body fat percentage decreased by just 5% instead of 10%, the outcome of the diet would appear quite dire. That would mean that half of the weight you lost was lean mass \u2013 10kg of fat mass, and 10kg of lean mass.<\/p>\n\n\n\n To be clear, that\u2019s how you should<\/em> interpret the results of those DEXA scans. If DEXA says you lost 14kg of fat and 6kg of lean mass, that actually means you lost somewhere between 10-18kg of fat, and somewhere between 2-10kg of lean mass. In other words, DEXA is telling you that the outcome of your diet was somewhere between \u201cunambiguously good\u201d and \u201ccatastrophically bad.\u201d<\/p>\n\n\n\n If you think you\u2019d be able to figure that out for yourself by simply looking in the mirror from time to time, then congratulations \u2013 you probably don\u2019t need to worry about assessing your body composition anymore. 2<\/a><\/sup>The prior sentence is probably painting with slightly too broad of a brush \u2013 when most people hear \u201cbody composition,\u201d they\u2019re generally thinking about estimates of fat mass, lean mass, and body fat percentage. I\u2019m writing with that particular connotation of the term in mind. However, assessments of bone mineral density and bone mineral content also fall under the umbrella of \u201cbody composition,\u201d and DEXA scans can actually estimate BMD and BMC quite accurately \u2013 if you have osteoporosis or osteopenia, or if you\u2019re at risk of osteoporosis or osteopenia, BMD and BMC assessments may be quite useful and informative. That\u2019s a conversation to have with your doctor, and it goes beyond the scope of this article.<\/span> Also, keep in mind that this is a pretty optimistic illustration. Rather than using DEXA, most people track their body composition over time using BIA scales, which produce even larger errors (both for assessing body composition at a single point in time, and for tracking changes in body composition over time). <\/p>\n\n\n\n As the prior section should make clear, we believe the evidence suggests that body composition data is generally too imprecise and inaccurate to be useful for individuals. However, if you\u2019re a MacroFactor user, I\u2019m sure you noticed that we ask for a rough estimate of your body fat percentage during the onboarding process, and I\u2019m sure you\u2019ve noticed that you can track your body fat percentage in-app. So, how does MacroFactor use body composition data?<\/p>\n\n\n\n We use your profile-level body fat percentage estimate for two things. Your profile-level body fat percentage estimate is what you entered during onboarding, and you can change it at any time<\/a> by going to \u201cMore\u201d \u2192 \u201cProfile\u201d \u2192 \u201cBody Fat %\u201d.<\/p>\n\n\n\n First, it\u2019s used to generate an initial total daily energy expenditure estimate. We use the Cunningham formula<\/a> to estimate your BMR, and lean body mass is the primary input in the Cunningham formula (estimating body fat percentage and estimating lean body mass are two sides of the same coin). <\/p>\n\n\n\n Second, it\u2019s used to generate protein recommendations. Protein needs generally tend to scale<\/a> with lean body mass<\/a>, so we need a rough estimate of your lean body mass to give protein recommendations for users on Coached macro programs.<\/p>\n\n\n\n If you track your body fat percentage day-to-day along with your weight, we don\u2019t use that data for anything. We simply allow users to track it for their own purposes.<\/p>\n\n\n\n This section will explain why we do<\/em> use body composition data for the two purposes listed above, and the next section will explain why we don\u2019t<\/em> use day-to-day body composition estimates for the purpose of making program adjustments.<\/p>\n\n\n\n As discussed in a previous article<\/a>, total daily energy expenditure estimates from static formulae are always pretty rough estimates. That\u2019s one of the primary reasons MacroFactor exists in the first place \u2013 a continuous stream of weight and nutrition data allows you to estimate your total daily energy expenditure (for the purpose of setting calorie intake targets to gain, lose, or maintain weight) far better than any static formula could. With that in mind, we opted to use the Cunningham formula<\/a> for two primary reasons.<\/p>\n\n\n\n First, we know that lean body mass is the primary predictor of energy expenditure<\/a>, so we figure it makes sense to just jump straight to the source. Other formulas (like the Harris-Benedict equation, for example) just use a mix of variables that happen to be predictive of energy expenditure because they tend to correlate with lean body mass (sex, age, weight, height, etc.).<\/p>\n\n\n\n Second, a pretty good chunk of MacroFactor users engage in resistance training, and the Cunningham equation does a particularly good job<\/a> of estimating energy expenditure for lifters. So, the Cunningham equation should produce energy expenditure estimates that are about as good as any other formula for the non-lifters who use MacroFactor, and better energy expenditure estimates than other formulas for the lifters who use MacroFactor.<\/p>\n\n\n\n Ultimately, we view the decision to use body composition data for this purpose \u2013 providing an initial energy expenditure estimate \u2013 to be a relatively minor decision. We are well aware that some users will overestimate their body fat percentage, and other users will underestimate their body fat percentage during onboarding, but a) errors in initial energy expenditure estimates are unavoidable, b) we allow users to override the app\u2019s initial estimate if they think it\u2019s too high or too low, and c) after about three weeks of consistent weight and nutrition tracking, the impact of this initial energy expenditure estimate will be fully washed out. Thus, if error is introduced due to poor body composition estimation, the effect will be relatively minor and short-lived.<\/p>\n\n\n\n Furthermore, data suggests<\/a> that when people misestimate their body fat percentage, they\u2019re most likely to think they\u2019re leaner than they actually are. And ultimately, if MacroFactor misestimates energy expenditure for a user during onboarding, I\u2019m far more comfortable with overestimates than large underestimates.<\/p>\n\n\n\n For example, imagine someone has a total daily energy expenditure of 3000 calories, and they\u2019re aiming to lose two pounds per week (which would require an average daily energy deficit of approximately 1000 calories). If we initially estimated that their daily expenditure was 3500 calories, and recommended that they consume 2500 calories per day, their initial rate of weight loss would be slower than intended (about 1 pound per week instead of 2 pounds per week), but they certainly wouldn\u2019t feel like they were being thrown into the deep end with an unsustainable diet, and MacroFactor\u2019s algorithms would be able to gradually reduce their calorie recommendations until they were losing two pounds per week.<\/p>\n\n\n\n Now, let\u2019s run this scenario back, but let\u2019s assume that we initially estimated that their daily energy expenditure was 2500 calories per day \u2013 a 500-calorie underestimate instead of a 500-calorie overestimate. In this scenario, their initial calorie recommendation would be just 1500 calories per day (consistent with losing three pounds per week, given their true energy expenditure of 3000 calories per day). That\u2019s a really<\/em> steep deficit. And sure, MacroFactor\u2019s algorithms would be able to gradually increase calorie recommendations until their rate of weight loss decreased to two pounds per week, but there would be a much greater risk of the user (understandably) viewing the diet as unsustainable and losing motivation within the first couple of weeks.<\/p>\n\n\n\n Ultimately, we always strive to provide the most accurate recommendations possible, so we certainly don\u2019t aim to overestimate users\u2019 initial energy expenditures. However, in most circumstances, initial overestimates are preferable to initial underestimates. So, if there\u2019s a tendency for users to underestimate their body fat percentage (or overestimate their activity levels) during onboarding, and subsequently eat a few extra calories during their first few weeks using MacroFactor, I\u2019m very comfortable with that. <\/p>\n\n\n\n Body composition estimates are used to inform protein recommendations to ensure that MacroFactor\u2019s nutrition recommendations will be tolerable and appropriate for users across the full spectrum of human body composition.<\/p>\n\n\n\n In the evidence-based fitness community, protein recommendations are generally provided in terms of grams of protein per kilogram of total body mass. The go-to reference is a meta-analysis by Morton and colleagues<\/a>, finding that about 1.6-2.2 grams of protein per kilo of total body mass (0.73-1g\/lb) was likely sufficient to maximize lean body mass.<\/p>\n\n\n\n Realistically, this 1.6-2.2g\/kg range is an excellent heuristic for a lot of people, but it\u2019s not perfectly precise or universally generalizable. For example, several studies<\/a> with protein intakes below 1.6g\/kg have reported similar gains in lean mass as studies with much higher protein intakes. More importantly, it can result in some unrealistically high protein recommendations for people who are quite heavy. If you weigh 150kg and have a reasonably high body fat percentage, there\u2019s probably no reason you need to consume 330 grams of protein per day.<\/p>\n\n\n\n Instead, MacroFactor\u2019s protein recommendations are scaled to lean body mass. Subjects in the types of studies included in the Morton meta-analysis are generally in the neighborhood of 20% body fat, allowing us to scale the protein recommendations from that meta-analysis (provided in terms of grams of protein per kilogram of total body mass) to protein recommendations per kilogram of lean body mass easily enough. So, that range of 1.6-2.2 grams of protein per kilogram of total<\/em> body mass corresponds to a range of about 2-2.75 grams of protein per kilogram of lean<\/em> body mass. This calculated range is reinforced by additional mechanistic research<\/a> indicating that the estimated average protein requirement for male bodybuilders is around 2 grams of protein per kilogram of lean body mass. <\/p>\n\n\n\n In our view, scaling protein recommendations to lean body mass comes with a clear benefit and minimal drawbacks, even if very rough estimates of body fat percentage are used to inform our estimate of lean body mass.<\/p>\n\n\n\n The key benefit, again, is that scaling protein recommendations to lean body mass ensures that protein doesn\u2019t \u201ccrowd out\u201d too much dietary fat and carbohydrate for users with higher body fat percentages. In a vacuum, there\u2019s not necessarily anything wrong with eating more protein than you strictly need<\/em> for the amount of lean mass you have. However, diets tend to be more flexible, tolerable, and hedonically pleasing when protein-rich foods don\u2019t crowd out most other food groups.<\/p>\n\n\n\n The only drawback is that users could be recommended higher or lower protein targets than would be theoretically optimal if they misestimated their body fat percentage. However, as previously mentioned, this is a minor drawback.<\/p>\n\n\n\n To illustrate, let\u2019s assume that my body fat percentage is 25%. I currently weigh about 225 pounds (102kg). If I set up a coached program in MacroFactor and selected a moderate protein level, my protein intake recommendation would be 192g if I said I was 18-23% body fat, 177g if I said I was 24-30% body fat, and 165g if I said I was 30-34% body fat.<\/p>\n\n\n\n Of these three potential recommendations, 177g\/day is \u201cright,\u201d but eating 12 fewer grams of protein per day isn\u2019t going to materially impact my results, and eating 15 more grams of protein per day isn\u2019t going to crowd out that much dietary fat and carbohydrate from the rest of my diet. In other words, your protein recommendations are always going to be solid, unless you misestimate your body composition to a pretty astronomical degree. Furthermore, the user is always in control \u2013 if they want a higher or lower protein target, they can always select one (or just set up a collaborative program, where protein targets can be as high or low as they want).<\/p>\n\n\n\n In short, your estimated body composition doesn\u2019t impact much about your experience with MacroFactor. In the two instances where body composition data is used, we believe the clear upsides outweigh the potential downsides, even when acknowledging the potential for erroneous body fat percentage estimates.<\/p>\n\n\n\n If you\u2019ve made it to this point in the article, the primary reason MacroFactor doesn\u2019t use daily body fat estimates to inform nutrition recommendations should be fairly obvious \u2013 the quality of the data would be too low to be particularly useful.<\/p>\n\n\n\n Biological data is inherently noisy, because biological system (including the human body) are messy. When evaluating whether a particular data source can be used to provide actionable insights, you need to consider whether you can cut through the noise to pick up the signal in an underlying dataset in a timely manner.<\/p>\n\n\n\n In the case of weight data, there\u2019s plenty of noise, but there\u2019s also plenty of signal. If your weight is up or down by 2-3 pounds on a particular day, that may not mean much. However, if your weight is consistently trending up or down over a period of weeks, that provides you with a very strong indication that your body weight is truly increasing or decreasing. Furthermore, there\u2019s enough signal in weight data to reliably pick up on trends in a timely manner \u2013 over a period of days-to-weeks.<\/p>\n\n\n\n You can\u2019t say the same about body composition data because body composition estimates have far greater day-to-day variability (relatively speaking) than weight measurements. So, reliably picking up on trends would take weeks-to-months, instead of days-to-weeks. Even if you could<\/em> pick up on trends, you wouldn\u2019t be able to use those trends to accurately inform nutrition recommendations in a timely manner.<\/p>\n\n\n\n Furthermore, weight data is particularly useful because, with few rare exceptions (for example, lymphedema), weight trends reflect changes in stored chemical energy<\/a> over the short-to-medium term. The link between stored chemical energy and body mass is the reason that the calories in\/calories out model of weight regulation works<\/em>. Sure, water weight fluctuations can impact the number on the scale day-to-day, but if you\u2019re 20 pounds lighter today than you were three months ago, that almost necessarily means you\u2019ve truly lost weight, and there\u2019s less chemical energy stored in your body than there was three months ago.<\/p>\n\n\n\n The same can\u2019t necessarily be said about day-to-day body composition data.<\/p>\n\n\n\n For tracking changes in body composition over the short term, most people rely on either BIA or a formula that estimates body composition from circumference measurements (most people aren\u2019t getting daily DEXA scans). Beyond the general accuracy and reliability issues associated with these methods of assessing body composition, there are clear circumstances where these methods of assessing body composition will provide completely erroneous (not just noisy) \u201cinsights\u201d over a pretty long period of time.<\/p>\n\n\n\n Starting with BIA, refer to my previous description of how this technology works: \u201cThese devices pass a weak electrical current between electrodes, and measure how long it takes for the electrical current to pass through your body\u2019s tissues. Since muscle has a greater water content than fat tissue (~75% versus ~10%), the electrical current will travel a bit faster through leaner people. In essence, BIA is estimating the hydration level of the tissues an electrical current is passing through, in order to estimate the amount of each type of tissue (fat versus lean) the current is passing through.\u201d<\/p>\n\n\n\n Particularly astute readers may have had some questions when encountering that description: \u201cSure, fat may impede the electrical current to a greater extent than muscle, but don\u2019t all<\/em> tissues impede the electrical current to some degree? So, would a BIA device think you had more body fat if the electrical current simply had to flow through more total tissue (including more muscle tissue)?\u201d<\/p>\n\n\n\n The answer to both of these questions is a firm \u201cyes.\u201d In other words, if your body composition doesn\u2019t change at all, but you gain a significant amount of muscle, that additional muscle will<\/em> further impede the electrical current, so a BIA device will think you gained at least some fat.<\/p>\n\n\n\n If you have a BIA scale that lets you specify whether you\u2019re an athlete or not, you can test this out for yourself. As of the time of writing, my BIA scale says I\u2019m 19.8% body fat if I say I\u2019m an athlete, and 30.3% body fat if I say I\u2019m not an athlete. That\u2019s a pretty huge divergence.<\/p>\n\n\n\n The reason for this divergence is that athletes tend to have considerably more lower-body muscle mass than non-athletes. If you tell your BIA scale that you\u2019re an athlete, it will anticipate greater impedance of the electrical current, because it will assume that the current is flowing through more total muscle tissue.<\/p>\n\n\n\n In other words, if a non-athlete at 30.3% body fat had an astoundingly successful period of body recomposition, they could lose a ton of fat and gain a ton of muscle at the same body weight, get down to 19.8% body fat, and their BIA scale wouldn\u2019t be able to tell the difference. In my case, that would mean that I could lose nearly 24 pounds of fat and gain nearly 24 pounds of muscle without my \u201csmart scale\u201d being any the wiser.<\/p>\n\n\n\n This same arithmetic works in reverse \u2013 an athlete could stop working out, lose a ton of muscle, gain a ton of fat, and still be told by their BIA scale that their body fat percentage was unchanged.<\/p>\n\n\n\n The only way the scale would know to interpret the impedance values differently would be if the user changed the athlete\/non-athlete toggle at some point in the process \u2013 at which point their day-to-day estimate of body fat percentage would increase or decrease by >10% in a single day. In other words, changes in body composition estimates from BIA scales could differ from true changes in body composition for a period of months or even years. This potential long-term tracking error compounds the issues of poor accuracy and reliability. As such, it doesn\u2019t seem prudent to use day-to-day body composition estimates from BIA to inform MacroFactor\u2019s algorithms.<\/p>\n\n\n\n The most popular circumference-based formula used to estimate body composition is the US Navy\u2019s formula<\/a>.<\/p>\n\n\n\n The Navy\u2019s formula requires two circumference measurements for men \u2013 waist and neck circumference \u2013 and three circumference measurements for women \u2013 waist, hip, and neck circumference.<\/p>\n\n\n\n In this formula, waist and hip measurements are assumed to positively correlate with body fat percentage, since males tend to store fat around their waist, and females tend to store fat around their waist and hips. Furthermore, neck circumference measurements are assumed to negatively<\/em> correlate with body fat percentage. It\u2019s assumed that neck circumference is a general proxy for muscularity and the size of an individual\u2019s frame. So, if two people have the same waist circumference, but one person has a larger neck, the Navy formula will assume that the large-necked individual is leaner.<\/p>\n\n\n\n The main drawback to this approach for estimating changes in body composition over time is that changes in neck circumference don\u2019t really reflect changes in general muscularity or the size of one\u2019s frame over time.<\/p>\n\n\n\n So, for example, if you started doing some resistance training for your neck musculature, the Navy\u2019s formula would say that you were getting substantially leaner, even if your body composition wasn\u2019t changing. More worryingly, some people store a decent amount of fat around their neck \u2013 for those individuals, decreases in neck circumference are a positive indicator of fat loss<\/em>, but the Navy formula would interpret the decrease in neck circumference as an indicator of fat gain<\/em>. The inverse is also true \u2013 if you happen to store a decent amount of fat around your neck, you may be able to gain a significant amount of fat without the Navy formula suggesting that your body fat percentage was increasing very much, because increases in waist and hip circumference would be partially (or entirely) \u201coffset\u201d by increases in neck circumference.<\/p>\n\n\n\n As with BIA, changes in body composition estimates derived from circumference measurements could fully conflict with actual changes in body composition over reasonably long time scales. <\/p>\n\n\n\n In short, the two most feasible methods for frequently estimating body composition have the potential to be mis<\/em>informative \u2013 not just uninformative \u2013 over periods of weeks to months. At best, daily body composition estimates require a relatively long observation window to separate the signal from the noise. At worst, daily body composition estimates can provide you with a completely false signal. As such, we don\u2019t think it would be prudent to incorporate this data into MacroFactor\u2019s coaching algorithms, or use it for the purpose of goal-setting.<\/p>\n\n\n\n Even if a user is primarily using MacroFactor to change their body composition (rather than simply to gain or lose weight), we believe that it makes the most sense to focus on the weight-related goal that generally accompanies the desired change in body composition (weight loss for people aiming to lose fat, weight maintenance for people aiming to recomp, and weight gain for people aiming to gain muscle), along with the behavioral factors that make a successful body composition-related outcome more likely (activity levels, adequate sleep, resistance training, self-monitoring, etc.). This approach is more likely to lead to a successful outcome than directly focusing your attention on changes in estimated body composition.<\/p>\n\n\n\n Since MacroFactor doesn\u2019t use day-to-day body composition estimates for program adjustments, some users may wonder how MacroFactor handles periods of body recomposition when a user is simultaneously losing fat and gaining muscle.<\/p>\n\n\n\n Fat tissue is more energetically dense than lean tissue<\/a> \u2013 lean tissue stores about 1800kcal per kilogram, while fat tissue stores about 9400kcal per kilogram, on average. So, in theory, you could be in a non-trivial energy deficit without a change in weight if you were simultaneously losing fat and gaining muscle. This principle also applies in reverse \u2013 if you lost a kilogram of muscle while gaining a kilogram of fat, that would represent a net surplus of approximately 7600kcal without a change in total body weight.<\/p>\n\n\n\n So, how would body recomposition affect your energy expenditure estimate and nutrition recommendations in MacroFactor?<\/p>\n\n\n\n I\u2019ll illustrate using an uncharitable scenario of considerably better-than-average body recomposition. In other words, this scenario would essentially be the worst-case scenario for MacroFactor\u2019s algorithms, and represent a practical upper limit on the degree to which body recomposition could \u201cconfuse\u201d MacroFactor\u2019s estimate of your total daily energy expenditure.<\/p>\n\n\n\n Over about 10 weeks of resistance training, untrained men gain about 1.6kg of lean body mass, on average<\/a>. For this scenario, we\u2019ll assume that an individual is gaining lean mass at twice that rate \u2013 3.2kg of lean mass over 10 weeks. Typically, such a gain in lean body mass would accompany a gain in total body mass, but we\u2019ll assume that this individual is not only gaining lean mass at twice the typical rate; they\u2019re also fully recomping. Thus, they don\u2019t gain any weight at all, but they lose 3.2kg of fat mass over 10 weeks. This is truly a pie-in-the-sky dream scenario for virtually any lifter \u2013 an extreme amount of body recomposition, in a relatively short period of time. Finally, although it\u2019s not too important for this particular illustration, we\u2019ll assume that this individual is consuming 3000kcal per day. <\/p>\n\n\n\n In this scenario, MacroFactor would see that this individual is consuming 3000 calories per day, and not experiencing any change in body mass. Therefore, their estimated energy expenditure would be 3000 calories per day. Simple enough.<\/p>\n\n\n\n However, we know that MacroFactor\u2019s estimate of this individual\u2019s energy expenditure is incorrect \u2013 since they\u2019re losing (calorically dense) fat tissue and gaining (less calorically dense) lean mass at the same rate, they must be in a net energy deficit. In other words, we know that they\u2019re expending more than 3000 calories per day.<\/p>\n\n\n\n We can calculate this individual\u2019s weekly energy deficit easily enough. They\u2019re gaining 0.32kg of lean mass per week, and losing 0.32kg of fat mass per week. Gaining 0.32kg of lean mass means this individual is storing a net of 576 calories per week (0.32kg * 1800kcal\/kg) in their newly synthesized lean tissue, and expending a net of 3008 (0.32kg * 9400kcal\/kg) from their catabolized fat tissue. Thus, each week, they\u2019re in a net caloric deficit of 3008 – 576 = 2432kcal. A weekly deficit of 2432kcal represents a daily deficit of about 347 calories. Therefore, this individual\u2019s actual<\/em> energy expenditure would be 3347kcal\/day (not 3000kcal\/day).<\/p>\n\n\n\n To put things in perspective, that magnitude of error (347 calories per day, or approximately 10%) represents an extreme edge case. With a more typical rate of body recomposition \u2013 for example gaining 1kg of lean mass while losing 1kg of fat over 10 weeks \u2013 the error would only be about 108 calories per day (or about 3.5%). Furthermore, this worst-case scenario for the accuracy of MacroFactor\u2019s algorithms still compares favorably to other methods someone might use for estimating their daily energy expenditure. Wearable devices under- or over-estimate total energy expenditure by more than 10% around 82% of the time<\/a>, and static equations used to estimate total daily energy expenditure have an average<\/em> error of ~325 calories per day<\/a>.<\/p>\n\n\n\n Thankfully, this inaccuracy doesn\u2019t actually have a negative impact for anyone pursuing a weight-related goal \u2013 or even a goal related to body recomposition \u2013 when using MacroFactor.<\/p>\n\n\n\n For example, if someone is aiming to lose a pound per week (which typically requires someone to consume about 500 fewer calories per day than their maintenance intake), it makes the most sense to calculate their calorie intake targets based on the level of calorie intake that results in weight maintenance, rather than the individual\u2019s \u201ctrue\u201d total daily energy expenditure.<\/p>\n\n\n\n In the scenario above, a daily caloric intake of about 2500 calories would result in about a pound of weight loss per week, based on the fact that an intake of 3000 calories per day results in weight maintenance. If we used an energy expenditure figure of 3347 calories per day to recommend a calorie intake target for losing a pound per week, the resulting intake target \u2013 2847 calories per day \u2013 would almost certainly be too high for the user to lose weight at their desired rate.<\/p>\n\n\n\n Similarly, if someone is specifically aiming for body recomposition \u2013 targeting weight maintenance, a very slow rate of weight gain, or a very slow rate of weight loss to simultaneously gain muscle and lose fat \u2013 baking in the energy expenditure estimation \u201cerror\u201d resulting from body recomposition is actually preferable when calculating energy intake targets.<\/p>\n\n\n\n In the scenario above, the user was simultaneously building muscle and losing fat while consuming 3000 calories per day. If they want to keep gaining muscle and losing fat while maintaining their current body weight \u2026 they should keep eating 3000 calories per day. In other words, the expenditure estimation error is the result<\/em> of body recomposition. There\u2019s no need to correct that error, because the energy expenditure estimation error is the outcome<\/em> of achieving your desired body recomposition, and the result<\/em> of the fact that your current intake targets are completely appropriate for your goals. The old adage \u201cdon\u2019t fix what isn’t broken\u201d perfectly describes this situation. <\/p>\n\n\n\n Although it\u2019s a challenge to accurately measure your precise body composition, you may still be interested in tracking some<\/em> sort of quantitative metric(s) to gauge the relative success of a weight gain or weight loss attempt.<\/p>\n\n\n\n With that in mind, these would be my recommendations:<\/p>\n\n\n\nWhat is actually being measured?<\/h2>\n\n\n\n
Body density<\/h4>\n\n\n\n
Subcutaneous fat thickness<\/h4>\n\n\n\n
Impedance of electrical currents<\/h4>\n\n\n\n
Body geometry<\/h4>\n\n\n\n
Gray pixels <\/h4>\n\n\n\n
![\"What](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/2-2.png\")
<\/a><\/figure>\n\n\n\nAccuracy of Body Composition Assessments<\/h2>\n\n\n\n
![\"Relationship](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/figure-2.png\")
<\/a><\/figure>\n\n\n\n
\n\n\n\n![\"Absolute](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/figure-3.png\")
<\/a><\/figure>\n\n\n\n![\"Interpreting](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/figure-4.png\")
<\/a><\/figure>\n\n\n\nHow MacroFactor Uses Body Composition Data<\/h2>\n\n\n\n
Initial Total Daily Energy Expenditure Estimate<\/h4>\n\n\n\n
![\"Relationship](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/figure-5.png\")
<\/a>Protein recommendations<\/h4>\n\n\n\n
![\"Relationship](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/figure-6.png\")
<\/a>Why MacroFactor Doesn\u2019t<\/em> Use Day-To-Day Body Fat Estimates To Inform Recommendations<\/h2>\n\n\n\n
Further Problems with BIA<\/h4>\n\n\n\n
Further Problems with Body Composition Estimates from Circumference Measurements<\/h4>\n\n\n\n
How Does Recomping Affect the Performance of MacroFactor\u2019s Algorithms?<\/h2>\n\n\n\n
![\"How](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/4-2.png\")
<\/a><\/figure>\n\n\n\n![\"Calculating](\"https:\/\/macrofactor.com\/wp-content\/uploads\/2023\/11\/3-2.png\")
<\/a><\/figure>\n\n\n\nWhat you should<\/em> measure if you\u2019d like to track body composition<\/h2>\n\n\n\n
1. Waist circumference<\/h4>\n\n\n\n