Body Fat Monitoring Made Ridiculously Simple

Body fat determination is not straightforward. This article will briefly review the major techniques, finishing with a simple, easily employed method.

Assessment of body fat levels has increasingly assumed greater importance in recent years. From a health standpoint, the traditional body mass index (BMI - weight/height)2, is not very informative since body composition does not enter into the calculation 29,38,39. Recent evidence has also suggested that body fat distribution is critical for accurate assessment of various health risks 2,4,11,16,22,24,35,36; therefore the overall level of adiposity is needed as a starting point.

In addition, since with physical training, body fat can decline while muscle mass can increase, net changes in body weight cannot reliably predict body fat levels. Changes in body fat levels can be followed as part of charting progress during training.

Also, absolute body fat levels can be used as goals such as disappearance of male "love handles" around 15% body fat or the appearance of an abdominal vertical midline indentation in women around 20%.

Unfortunately, body fat determination is not straightforward compared to height, weight, aerobic fitness, or strength determinations. Multiple methodologies have been developed, each with their advantages as well as limitations. This article will briefly review the major techniques, finishing with a simple, easily employed method, requiring nothing more than a tape measure and a look up table that should be more than adequate for most individuals.

The Different Ways For Body Fat Assessments

The 'gold standard' for body fat assessment is hydrostatic weighing 18, which involves being completely submerged underwater after expelling as much air from your lungs as possible. Typically, at least 4 or 5 attempts are necessary to achieve a stable weight measurement. Obviously, not everyone can comply with the procedures, nor is the requisite equipment commonly available. A more recent methodology introduced that is qualitatively similar to underwater weighing is the Bod PodTM 28,37. This device measures total body volume by air displacement in a specialized enclosed chamber. While technically easier than underwater weighing, this equipment is currently even less available than underwater weighing.

Both of the previous methods seek to measure overall body density. The human body is assumed to consist of two compartments, fat mass and lean body mass with different densities. Body fat is then calculated based on accepted values for the density of fat and lean tissue to identify the percentage of body fat that will produce the overall measured density 6,34. Errors arise both from measurement errors, as well as variations in lean tissue density. Variation in lean tissue density have been associated with age, gender, physical activity, and ethnicity. Variation (between individuals) arises due to the lumping of all lean tissue into one homogenous mass. In fact, four compartment models have been introduced to yield better results 9,12,17. In this case, fat mass is still one compartment, but the lean tissue is divided into three discrete compartments, bone, muscle, and everything else. Differences in bone density probably account for much of the variation due to age, activity, and ethnic differences 8,27.

Another recently introduced method is called dual-energy x-ray absorptiometry or DEXA (also DXA) for short. DEXA uses a three compartment model dividing the body into total body mineral, mineral-free lean tissue, and fat mass 14,26,30. Agreement with earlier methods is good; however, DEXA suffers from standardization issues at present. Results can vary with the specific equipment manufacturer, data collection methods, and software analysis 31. In the future, as methodologies are standardized and the equipment becomes more widely available, DEXA may develop into the medical body fat assessment technique of choice, partly due to its simplicity from the standpoint of the subject as well as providing valuable information regarding bone mineral content (important for osteoporosis evaluation).

For a method that is still somewhat high tech, but quite approachable, bioelectrical impedance analysis or BIA is relatively common 23. This method involves passing a small current through the body (which is painless) to measure the body's electrical resistance which is reflective of total body water. Since fat tissue has a distinct resistance compared to lean tissue (due to its different water content), the percentage of fat tissue can be determined from the overall body resistance and total weight.

Overall, BIA is somewhat reliable provided certain, stringent conditions have been met such as, no eating or drinking prior to the test (about 4 hours), no exercise for 12 hours before the test, no alcohol consumption for 48 hours, and no diuretic usage 18. Obviously, all of these restrictions involve potential changes in body water for which the test is uniquely sensitive. If using BIA analysis to track training or dieting progress over time, analysis should be conducted no more frequently than weekly under identical conditions (as reasonably feasible as possible): first thing in the AM after urination is suggested.

Typical BIA measurements involve measuring between wrist and foot (necessitating removal of shoes and socks). Two personal units, the TanitaTM ( and OMRONTM ( analyzers, measuring lower body and upper body resistance, respectively are also commercially available. While reasonably priced for home use, these devices utilize proprietary prediction equations which have not been extensively validated under research settings with a variety of populations, except as claimed by the manufacturers. Users may also be confused by the settings of adult versus athlete. This distinction is an attempt to account for the greater muscle and bone mass of athletes, which as discussed above can influence results due to differences in water content between muscle, bone and other lean tissues.

Unfortunately, advice for determining whether to use the regular or athletic formula is based on duration of weekly activity which limits utility for tracking progress with increasing training load, since it's not obvious when someone changes from a mere adult to an athlete. In some cases, the results can vary substantially between the two settings which is more reflective of the different populations used to derive the predictive equations, rather than attributes of the test subject. Thus, while the absolute body fat level may be uncertain, these devices can track changes in the level over time.

Moving further down the technological spectrum, we come to skin fold measurements. Various systems have been developed using measurements anywhere from 1 to 7 distinct sites on the body15. Since about 80% of total body fat is located subcutaneously 3, this method can be quite accurate. The only major fat store inaccessible by this measure is the intra-abdominal deposit. This store typically accumulates with age, and so many skin fold equations incorporate an age factor.

However, because this age effect is related more to the typical populations examined, rather than a specific biological effect due to aging itself, someone who is atypical with regard to abdominal fat accumulation (from simply having controlled their weight better than average as they have aged) will have skewed results.

One potential drawback to skin fold measurements is that some degree of training is required, although most health clubs usually have trained personnel that are more than happy to pinch your skin. Another drawback is that one cannot perform skin fold measurements on themselves.

Finally, the simplest methodology of all is exceedingly low tech and utilizes a tape measurement. Girth measurements are quite simple to perform and highly reliable and reproducible 7. Girth measurements can also be performed privately and solo. While girth measurements are correlated with overall body fatness however; 25,39 single measurements such as a waist circumference are not sufficiently accurate to be used reliably, in spite of certain authors' claims to the contrary 5. In particular, for women who deposit fat primarily lower on the body (hips, buttocks, and thighs), waist measurements in isolation are quite unreliable. Waist circumference ranges have been used to predict risk of body fat related diseases, but again, this is merely correlational with risk level, rather than predictive of the absolute fat level 39.

One basis for single girth measurement unreliability, is bone structure. Increasing pelvic width, which implies a larger bone structure is associated with greater overall weight, but not necessarily with greater adiposity 32. To account for bone structure or frame size as it is commonly called, some measure of bone structure can be added. The standard life insurance weight table arbitrarily divides people into small, medium, and large frames, although a quantitative measure of frame determination is usually not provided (fingers around the wrist do not take into account people with long or short fingers). Wrist circumference or elbow breadth have been used as quantitative measures of frame size 8.

Barry Sears added the dimension of wrist circumference along with waist circumference with a tabular set of values to determine body fat percentage in his book, Enter the Zone 33. Unfortunately, Sears does not provide details on the origin of his body fat percentage table, so the validity and applicability of his values cannot be independently verified (which is important since the specific populations utilized to derive the table may or not include different ethnic groups and variable age ranges). While frame size does add additional information, it is limited to making adjustments only for bone mass. Muscle mass is the other component of lean tissue that can vary tremendously among people. Bodybuilders in particular, have a much higher muscle mass than the average individual which places them significantly apart from the standard groups used to derive body fat tables.

The Military Method

With all these issues in mind, we now come to the simplest method for estimating body fat percentage. The US military has begun to examine body composition in their assessment of soldiers. Satisfactory, cautionary and unsatisfactory ranges for body fat percentages have been identified1. The cautionary range requires additional physical performance targets to be reached (in other words, a soldier could maintain a higher level of body fat, if that level did not interfere with athletic performance). The military collects extensive anthropometric data (body measurements of all types) on their personnel both from the standpoint of uniform design, as well as equipment design (for example, the cormic index is the ratio of sitting to standing height which is important for aircraft designers to know).

Each of the different services provided data on thousands of their own people and service specific equations were derived for both men and women by comparison with underwater weighing. Multiple anthropometric measurements were evaluated to the identify the best predictors for high correlation with underwater weighing results. In the end, the best results were obtained with equations that utilized height along with waist and neck circumferences in men and waist, hips, and neck circumferences in women1.

The inclusion of a neck girth requires some explanation. The neck is not a major site of fat deposition in most people, except in cases of extreme obesity. The neck includes some vertebral skeletal elements which would be expected to be influenced by frame size. Also, neck musculature, while not substantial, does connect with skeletal elements of the shoulder girdle and thus, should track along with overall muscle mass.

While there were multiple equations derived from the various service data sets, the Navy circumference method can be commonly found on many web sites and utilizes the Navy derived equations 19,20. The Navy equations are employed because among the different equations, they gave the best correlation to underwater weighing. However, in the 1990's, the military went further. They decided to collapse the data set into one huge collection and converted the equations to a tabular form (probably as a result of not anticipating the accessibility of handheld computers) with some additional simplification in reporting results 21.

So now let's go over in detail the basics of the military method (your tax dollars at work). The equations and corresponding tables are different for men and women as might be expected. Let's begin with men. Males need two girth measurements and their height. A waist measurement is made at the level of the navel (umbilicus or belly button) parallel to the ground. The second girth measurement is neck circumference, measured just below the adam's apple with a slight downward tilt (front slightly lower than back). The neck circumference is subtracted from the waist and that figure is used along with height to determine body fat percentage from the table.

For women, the neck measurement is performed similarly. Hips are also measured with the level determined by the greatest circumference over the buttocks and parallel to the floor. The waist is measured differently than men in that the smallest circumference is determined. The waist and hips measurements are added, then the neck measurement is subtracted and compared on the table with height.

The military was not interested in exacting precision. Measurements are made to the nearest half inch and rounded up for the neck and rounded down for both waist and hips. Body fat percentages are reported to the nearest whole percentage value. This is physiologically and statistically reasonable. Girth measurements cannot be expected to distinguish 15.1% versus 15.4% body fat reliably. In addition, the combination of food intake, hydration status, and variability in gastrointestional contents from variations in the diet (fiber for example retains water in the colon) suggests that an individual's weight can fluctuate within a 1% range throughout the day. As such, it would be difficult to assign a body fat percentage more accurately than this fluctuation range.

Another useful aspect of the tabular form (as opposed to the calculator) is that estimates can be made of girth targets to achieve a specific body fat percentage. For example, for men over the typical range of heights, a one-half inch change in waist circumference corresponds to a 1% change in body fat. Thus, one can estimate potential waist sizes if certain body fat percentage targets are reached.

Certain limitations with the military method should be discussed. Obviously, children cannot be measured with these tables. In addition, while the military has validated these results over a wide age range and ethnic diversity, it's clear that as with children, the data set is lacking for elderly men and women. In particular, postmenopausal women are certainly under represented. Finally, inspection of the tables indicates values below 9% for men and 19% for women are not reported. This likely represents a combination of factors.

The data set used to arrive at these values probably does not contain a statistically sufficient number of data points below those body fat levels to ensure accuracy. Other military data suggest lower body fat percentage limits for men in the 4 - 6 % range (representing essential body fat levels)13. This lower limit of body fat actually represents a minimal amount of body fat (in the 5 - 6 pound range for men), rather than a minimal percentage. For women, minimal body fat percentages are assumed to be about 10% higher.

While there are reports of lower body fat levels, especially among bodybuilders, these low numbers are partly due to higher than normal muscle mass. A body fat percentage represents the mass of body fat divided by total body weight. Even with a stable fat mass (whether or not it's close to the minimum), increasing overall mass by increasing muscle and/or bone will still make the body fat percentage decline. In addition, the inaccuracy of most methodologies in the low range of body fat percentage needs to be appreciated. For example, equations for skin fold thickness can predict an extremely low body fat level, but the data set from which the equation was derived is unlikely to have had valid data points in that low percent region; thus, body fat levels in that range are extrapolations which have greater degrees of error.

By the same analogy, the upper end of body fat percentages are also subject to greater measurement errors. However, since at extreme upper levels of body fat, weight loss heavily favors fat loss (with almost any form of caloric restriction) 10, there is less need to closely monitor changes in body fat levels until approaching the lower end of the obese range (males <35%, females <45%).


Two other points should be noted with regards to extreme low measures of body fat in addition to simple technical measurement issues. Most methodologies are derived from a relatively normal population who in general have not used steroids. Since testosterone influences muscle versus fat partitioning, pharmacologic levels of testosterone allow for lower body fat levels than achievable with physiologic levels of testosterone. Finally, even for those individuals who achieve ultra-low body fat percentages, that level is rarely maintained for periods longer than the time required for a contest or photoshoot to occur. Long term maintenance of ultra-low body levels is simply not feasible for most people.

With these simple, straightforward, measures, individuals can set training goals and track their progress with military precision (at least, good enough for government work) as well as add new meaning to the phrase, "lean, mean, fighting machine."

Reference List
  1. 1992. Body composition and physical performance: Applications for the military services. National Academic Press, Washington, DC.
  2. Albrinks, M. J. and J. W. Meigs. 1964. Interrelationship between skinfold thickness, serum lipids and blood sugar in normal men. Am. J. Clin. Nutr. 15:255-261.
  3. Arner, P. 1997. Regional adipocity in man. J. Endocrinol. 155:191-192.
  4. Blair, D., J. P. Habicht, E. A. Sims, D. Sylwester, and S. Abraham. 1984. Evidence for an increased risk for hypertension with centrally located body fat and the effect of race and sex on this risk. Am. J. Epidemiol. 119:526-540.
  5. Brown, R. 1999. The Body Fat Guide. Healthstyle Publications, Kitchener, Ontario, CA.
  6. Brozek, J., F. Grande, J. T. Anderson, and A. Keys. 1963. Densitometric analysis of body composition: revision of some quantitative assumptions. Ann. N. Y. Acad. Sci.113-140.
  7. Callaway, C. W., W. C. Chumlea, C. Bouchard, J. H. Himes, T. G. Lohman, and A. D. Martin. 1988. Circumferences., p. 39-54. In T. G. Lohman, A. F. Roche, and R. Martorelli (eds.), Anthropometric standardization reference manual. Human Kinetics, Champaign, Il.
  8. Chumlea, W. C., W. Wisemandle, S. S. Guo, and R. M. Siervogel. 2002. Relations between frame size and body composition and bone mineral status. Am. J. Clin. Nutr. 75:1012-1016.
  9. Clasey, J. L., J. A. Kanaley, L. Wideman, S. B. Heymsfield, C. D. Teates, M. E. Gutgesell, M. O. Thorner, M. L. Hartman, and A. Weltman. 1999. Validity of methods of body composition assessment in young and older men and women. J. Appl. Physiol 86:1728-1738.
  10. Dulloo, A. G. and J. Jacquet. 1999. The control of partitioning between protein and fat during human starvation: its internal determinants and biological significance. Br. J. Nutr. 82:339-356.
  11. Folsom, A. R., S. A. Kaye, T. A. Sellers, C. P. Hong, J. R. Cerhan, J. D. Potter, and R. J. Prineas. 1993. Body fat distribution and 5-year risk of death in older women. JAMA 269:483-487.
  12. Friedl, K. E., J. P. DeLuca, L. J. Marchitelli, and J. A. Vogel. 1992. Reliability of body-fat estimations from a four-compartment model by using density, body water, and bone mineral measurements. Am. J. Clin. Nutr 55:764-770.
  13. Friedl, K. E., R. J. Moore, L. E. Martinez-Lopez, J. A. Vogel, E. W. Askew, L. J. Marchitelli, R. W. Hoyt, and C. C. Gordon. 1994. Lower limit of body fat in healthy active men. J. Appl. Physiol 77:933-940.
  14. Hansen, N. J., T. G. Lohman, S. B. Going, M. C. Hall, R. W. Pamenter, L. A. Bare, T. W. Boyden, and L. B. Houtkooper. 1993. Prediction of body composition in premenopausal females from dual-energy X-ray absorptiometry. J. Appl. Physiol 75:1637-1641.
  15. Harrison, G. G., E. R. Buskirk, J. E. Lindsay Carter, F. E. Johnston, T. G. Lohman, and M. L. Pollock. 1988. Skinfold thickness and measurement technique., p. 55-70. In T. G. Lohman, A. F. Roche, and R. Martorelli (eds.), Anthropometric standardization reference manual. Human Kinetics, Champaign, Il.
  16. Hartz, A. J., D. C. Rupley, and A. A. Rimm . 1984. The association of girth measurements with disease in 32,856 women. Am. J. Epidemiol. 119:71-80.
  17. Heymsfield, S. B., S. Lichtman, R. N. Baumgartner, J. Wang, Y. Kamen, A. Aliprantis, and R. N. Pierson, Jr. 1990. Body composition of humans: comparison of two improved four-compartment models that differ in expense, technical complexity, and radiation exposure. Am. J. Clin. Nutr 52:52-58.
  18. Heyward, V. 2001. ASEP recommendation: Body composition assessment. J. Exer. Physiol. 4:1-12.
  19. Hodgdon, J. A. and Beckett, M. B. 1984. Prediction of percent body fat for U.S. Navy women from body circumference and height. Report No.84-29. San Diego, CA, Naval Health Research Center.
  20. Hodgdon, J. A. and Beckett, M. B. 1984. Prediction of percent body fat for U.S. Navy men from body circumference and height. Report No. 84-11. San Diego, CA, Naval Health Research Center.
  21. Hodgdon, J. A. and Friedl, K. 1999. Development of the DoD body composition estimation equations. Report No. 99-2B. San Diego, CA, Naval Health Research Center.
  22. Kissebah, A. H., N. Vydelingum, R. Murray, D. J. Evans, A. J. Hartz, R. K. Kalkhoff, and P. W. Adams. 1982. Relation of body fat distribution to metabolic complications of obesity. J. Clin. Endocrinol. Metab 54:254-260.
  23. Kushner, R. F. 1992. Bioelectrical impedance analysis: a review of principles and applications. J. Am. Coll. Nutr 11:199-209.
  24. Larsson, B., K. Svardsudd, L. Welin, L. Wilhelmsen, P. Bjorntorp, and G. Tibblin. 1984. Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. Br. Med. J. (Clin. Res. Ed) 288:1401-1404.
  25. Lean, M. E., T. S. Han, and P. Deurenberg. 1996. Predicting body composition by densitometry from simple anthropometric measurements. Am. J. Clin. Nutr 63:4-14.
  26. Mazess, R. B., H. S. Barden, J. P. Bisek, and J. Hanson. 1990. Dual-energy x-ray absorptiometry for total-body and regional bone-mineral and soft-tissue composition. Am. J. Clin. Nutr 51:1106-1112.
  27. Mazess, R. B., H. S. Barden, P. J. Drinka, S. F. Bauwens, E. S. Orwoll, and N. H. Bell. 1990. Influence of age and body weight on spine and femur bone mineral density in U.S. white men. J. Bone Miner. Res. 5:645-652.
  28. McCrory, M. A., T. D. Gomez, E. M. Bernauer, and P. A. Mole. 1995. Evaluation of a new air displacement plethysmograph for measuring human body composition. Med. Sci. Sports Exerc. 27:1686-1691.
  29. Michels, K. B., S. Greenland, and B. A. Rosner. 1998. Does body mass index adequately capture the relation of body composition and body size to health outcomes? Am. J. Epidemiol. 147:167-172.
  30. Prior, B. M., K. J. Cureton, C. M. Modlesky, E. M. Evans, M. A. Sloniger, M. Saunders, and R. D. Lewis. 1997. In vivo validation of whole body composition estimates from dual-energy X-ray absorptiometry. J. Appl. Physiol 83:623-630.
  31. Roubenoff, R., J. J. Kehayias, B. Dawson-Hughes, and S. B. Heymsfield. 1993. Use of dual-energy x-ray absorptiometry in body-composition studies: not yet a "gold standard". Am. J. Clin. Nutr 58:589-591.
  32. Ruff, C. 2002. Variation in human body size and shape. Annu. Rev. Anthropol. 31:211-232.
  33. Sears, B. 1995. Enter the Zone. HarperCollins Publixhers, Inc., New York, NY.
  34. Siri, W. E. 1961. Body composition from fluid spaces and density: analysis of methods., p. 223-244. In J. Brozek and A. Henschel (eds.), Techniques for measuring body composition. National Academy of Sciences, Washington, DC.
  35. Stevens, J., J. E. Keil, P. F. Rust, H. A. Tyroler, C. E. Davis, and P. C. Gazes. 1992. Body mass index and body girths as predictors of mortality in black and white women. Arch. Intern. Med. 152:1257-1262.
  36. Stevens, J., J. E. Keil, P. F. Rust, R. R. Verdugo, C. E. Davis, H. A. Tyroler, and P. C. Gazes. 1992. Body mass index and body girths as predictors of mortality in black and white men. Am. J. Epidemiol. 135:1137-1146.
  37. Wagner, D. R., V. H. Heyward, and A. L. Gibson. 2000. Validation of air displacement plethysmography for assessing body composition. Med. Sci. Sports Exerc. 32:1339-1344.
  38. World Health Organization. 1997. Obesity, preventing and managing the global epidemic - report of a WHO consultation on obesity. Geneva, WHO.
  39. Zhu, S., Z. Wang, S. Heshka, M. Heo, M. S. Faith, and S. B. Heymsfield. 2002. Waist circumference and obesity-associated risk factors among whites in the third National Health and Nutrition Examination Survey: clinical action thresholds. Am. J. Clin. Nutr. 76:743-749.