Skinfold Calipers

How to calculate body fat percentage

Though many equations have been developed in an attempt to improve the measurement accuracy of skinfold calipers, the following equations were developed by Siri (1961). These equations are just one example of how this can be done, however, other equations are specifically targeted to gender, age group, and other types of populations (e.g. sport-specific).

Body Density (men) = 1.112505 – 0.0013125 * X3 + 0.0000055* (X3) ^2 – 0.000244*age

Where X3 is the sum of the following skinfolds: triceps, chest, and subscapular.

Body Density (women) = 1/089733 – 0.0009245*X3 + 0.0000025*(X3) ^2 – 0.0000979*age

Where X3 is the sum of the following skinfolds: triceps, supra-illiac, and abdominal. (Age is always in years).

Once the body density has been determined, the percentage of fat mass (body fat percentage) is calculated by applying equations of Siri (Siri, 1961):

Percent fat = ((4.95/Body Density))-4.5) * 100.

Most predictive equations have a standard error of estimate (SEE) from ± 3% to ± 7%. For example, common equations used alongside skinfolds like Jackson and Pollock, or Durnin and Womersley’s sex- and age-specific equations, all produced similar percentage body fat percentages to DEXA and underwater weighing (UWW) techniques as seen in Figure 3 but did tend to under-predict on average.

Figure 3. Body fat % by anthropometric predictions, DXA, and UWW in adult White females (Wang et al., 2006).

Figure 4. Body fat % by anthropometric predictions, DXA, and UWW in adult White males (Wang et al., 2006).

Validity and Reliability of Skinfold Calipers

As skinfold calipers are quick, easy-to-use, and very affordable for estimating body fat percentage, they have become more widely used over the years (Wells, 2005). This has happened despite newer techniques such as dual-energy x-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), computerized tomography (CT), and bioelectrical impedance analysis (BIA) all having been developed (Wang et al., 2006).

One study in 2005 by Eston et al (2005) investigated the relationship between body fat percentage, measured by skinfolds, versus DEXA, and found that skinfolds were “highly related to the percent body fat in fit and healthy young men and women”; especially the thigh skinfold, which showed the highest correlation with total percent fat.

Another study evaluating the validity of body fat measured by skinfolds, ultrasound, and BIA compared to DEXA, found that in 208 young men and women, skinfolds were highly correlated to DEXA results (r = 0.91-0.92), with a mean difference between both measures of 6.9 ± 0.4 percent body fat (Duz, Kocak, & Korkusuz, 2009). Furthermore, skinfolds tended to underpredict body fat percentage as compared to DEXA, revealing that DEXA and skinfold could not be used interchangeably. According to this study, and others (Duz et al., 2009; Lean, M., Han, S, Deurenberg, 1996), skinfolds may have a significant bias at extremes of body fat and age.

The best use of skinfolds seems to be their raw values (i.e. the summation of all measurement sites in millimetres), rather than their ability to predict total body fat percentage because there are errors associated with the accuracy of the collection of the raw data, and error in assumptions in the final values (Wells, 2005). Raw skinfold data can give us a good idea of the regional fatness, unlike other measures like BMI or circumference measures alone (Jackson, Pollock, Graves, & Mahar, 1988; Wells, 2005).

For some populations, such as athletic populations, where the difference of one percentage point of body fat can make a difference in performance, skinfolds are likely more important (Ransdell & Murray, 2011). For overweight or obese populations, taking skinfolds may be of less use, as accuracy and reliability of the skinfold measurements will be harder to repeat as the skinfold thickness increases, so methods like DEXA may be more accurate (Donini et al., 2013). Other studies, for example on obese children, have found good agreeance between skinfolds and percent fat measured by DEXA (Wohlfahrt-Veje et al., 2014), however, considerations based on the population being measured must be addressed by each case separately.

Technical Error of Measurement (TEM)

As with any good science, if the experiment can be repeated many times over (repeatability),and is valid (the attempt to measure what we think we’re measuring, otherwise known as accuracy), the research is likely good research and helps contribute to our better understanding of a topic.

In anthropometry, technical error of measure (TEM) is what we refer to the error that occurs when a measurement is taken on the same object more than once, and the values are not the same. This error is inherent especially when humans are involved in the measurements, due to:

• Biological variation of the subject (day-to-day)
• Biological variation of the subject (within-day)
• Instrument fault (not calibrated properly or not functioning properly), or
• Human measurement fault (intra- and inter-tester error)
• Environment (change of testing site setting or location)

We want to minimise the error in our measurement as much as possible to create the most accurate and reliable measurement possible each time, but all errors cannot usually be removed (Perini, de Oliveira, Ornelia, & de Oliveira, 2005). To minimise these factors, it is best that we control as many factors as possible, and use the same tester, the same location, the same time of day and day of the week, and a consistent schedule throughout the week (in training and diet) (Perini et al., 2005).

Because we know the error is associated with the measurements, practitioners should always express their measures as a value with the technical error, so that when measuring change over time, we can be more certain of real change versus errors made in measuring. To calculate the technical error, use the following equations, outlined in a paper by Perini et al, (2005):

Figure 5. How to calculate the Absolute TEM (Perini et al., 2005).

Figure 6. How to calculate the relative TEM (Perini et al., 2005).

Figure 7. Acceptable levels for intra- and inter-evaluator error, according to a beginner (Level 1 ISAK) versus a skilful anthropometrist (Level 4 ISAK) according to the International Society for the Advancement of Kinanthropometry (Norton, K., Olds, T., Olive, S., Craig, 2000).

Finally, to make measurements of body composition more accurate, ensure the use of predictive body fat percentage equations that best match the demographic of the persons you are testing.

Conclusion

Generally, the understanding of the use of skinfold calipers and their accuracy is very poor and grossly misunderstood. Given this, our mission was to clarify whether skinfolds are a good method of choice for body composition.

In conclusion, skinfold calipers can be a cost-effective, quick, and relatively accurate measure of body composition over time. While the gold standard for body composition is still cadaver dissection, skinfold measurements can offer information about the relative fatness, the change in body composition over time, and potentially even the health of the individual.

Knowing that increased fat mass is associated with various diseases (Donini et al., 2013), and some athletes need specific body fat percentages for optimal performance (Ransdell & Murray, 2011; Romero et al., 2009; Tanda & Knechtle, 2013), it is of importance that fitness professionals measure skinfolds accurately and with the ability to be repeatable, following the international standards (ISAK), for best results.