1. Summary
2. What is Bioelectrical Impedance Analysis?
3. Types of Bioelectrical Impedance Analysis
4. What are the Bioelectrical Impedance Analysis equations?
5. Is Bioelectrical Impedance Analysis valid and reliable?
6. Are there issues with Bioelectrical Impedance Analysis?
7. Is future research needed with Bioelectrical Impedance Analysis?
8. Conclusion
9. References
10. About the Author

Summary

Bioelectrical Impedance Analysis (BIA) is able to make an estimation of body composition (e.g. quantities of fat mass and fat-free mass) by running a small electrical current through the body. This is possible simply because different bodily tissues (e.g. muscle, fat, bone, etc.) all have varying amounts of water content, and, as a result, they all differ in terms of electrical conductivity.

Despite being popular in many commercial gyms and within epidemiological research on group body composition, BIA does not appear to provide valid single- or repeated-measures of body composition for athletes. Having said that, the development of an equation for athletic populations that are validated against the gold-standard four-compartment model may improve the validity of the measure.

What is Bioelectrical Impedance Analysis?

First commercially available in the mid-1980s [1], Bioelectrical Impedance Analysis (BIA) is an inexpensive and portable piece of body composition testing equipment. Although BIA was primarily used to determine changes in dialysis patients [2], it is a method now used to determine body composition across a range of populations, including athletes [2, 3], obese individuals [4, 5], and the general population [3].

BIA determines body composition by running small electrical currents through the body. As the electrical conductivity is different between various bodily tissues (e.g. muscle, fat, bone, etc.) due to their variation in water content, the small electrical current passes through the tissues at different speeds. Armed with that information, the machine is able to calculate the impedance (i.e. the resistance of the electrical current [Z]) of the current and then estimate body composition – hence the name “bioelectrical impedance”.

The principle of BIA is that the different tissues in the body will act as conductors, semiconductors, or dielectrics (insulators). Lean tissues are highly-conductive, as they contain large quantities of water. In contrast, bone and adipose tissue are dielectric substances and are poor conductors [4]. BIA assumes that the human body is composed of a series of cylinders, uniform in shape, length, cross-sectional area, and with constant conductivity.

Total body water (TBW) is estimated, and this estimation is used to calculate fat-free mass. This is done under the assumption that 73 % of the body’s fat-free mass is water and that this remains constant over time and between individuals. Fat mass is then calculated as the difference between fat-free mass and body mass.

Types of Bioelectrical Impedance Analysis

Several methods have been used to assess body composition in humans, each with advantages and drawbacks surrounding cost, validity, reliability, and accessibility.

Bioelectrical impedance methods are often classified by the number of frequencies used for analysis, with BIA often referring to single-frequency devices, whereas multiple-frequency devices are referred to as ‘Bioelectrical Spectroscopy’ (BIS) as it uses a ‘spectra’ of frequencies [5]. It is unclear how many frequencies would be needed for a BIA device to be considered a BIS device, however, the principles behind how the devices work are the same. Therefore, for this review, BIA will be used to denote all bioelectrical impedance assessments.

Hand-held BIA
Different types of BIA analysers are available, such as hand-held and leg-to-leg devices. Hand-held BIA machines assess the conductance of a small alternating current through the upper body and use built-in software to calculate body composition after it has been calibrated with the following variables: weight, height, age, and gender [6]. This method may be of benefit in a field setting, due to its convenience.

Leg-to-Leg BIA
Similar to hand-held methods, leg-to-leg BIA involves an individual standing on scales with four electrodes situated at each footplate, with a low-level current passed through the lower body. The path of the electrical current may differ between this method and the hand-held method, and could potentially influence body composition results; though this issue is discussed later in the article.

Hand-to-Foot BIA
Hand-to-foot BIA uses electrodes in a mounted footplate, as well as electrodes in hand grips, to determine whole-body measurements. As hand-held and leg-to-leg methods may not account for the resistance of the lower- or upper body, respectively, it is logical to assume that hand-to-foot measurements may better reflect whole-body composition than the alternatives.

What are the Bioelectrical Impedance Analysis equations?

Estimates of body composition using BIA are facilitated using empirically validated equations, which consider variables including gender, race, height, weight, and age. Consequently, it is important the correct equation is used for the population measured to ensure that any results are valid. It is also important to understand the reference assessment method used to validate these equations.

For example, many BIA equations are validated against assessment methods such as hydrostatic weighing and Dual-energy X-ray Absorptiometry (DEXA). However, these methods can also lead to error, as hydrostatic weighing has been shown to lead to individual error rates of 6 % [6]. From the results of this assessment method, the manufacturer constructs an equation using the individual variables mentioned previously to determine what the body fat would be.

These equations will have an error rate when compared to the hydrostatic weighing method, and thus, this error is multiplied by the original error of the reference method to provide a body composition assessment that may be somewhat distant from the actual values reported using a four-compartment model.

Is Bioelectrical Impedance Analysis valid and reliable?

The validity (the agreement between the true value and a measurement value) of body composition is key to determining the precision of BIA measurement, and its suitability for clinical use. The criterion method for determining body composition is the four-compartment model (1] fat mass, 2] total body water, 3] bone mineral mass, and 4] residual mass), and should be used when assessing the validity of BIA measurements.

BIA has been compared to the four-compartment model in several studies using various populations. Sun et al., [7] validated BIA equations using the four-compartment model and reported that the equation was sufficient for use in epidemiological research studies to assess populations with normal levels of body composition.

Sun et al. (2003) [8] stated that BIA is a suitable alternative for estimating body fat percentages when subjects are within a “normal” body fat range, however, there is a tendency for BIA to overestimate body fat in lean subjects and underestimate body fat in obese individuals. It is important to note that this analysis utilised DEXA as the reference method, which may also lead to further error, as eluded to earlier in this review (read my article on the use of DEXA scanning for body composition assessment HERE).

The validity of BIA for one-off measures of body composition
Despite studies showing promising effects of BIA on body composition, this has not been found in a large body of research. BIA has been shown to underestimate fat mass and overestimate fat-free mass by 1.9 and 1.8 kg in obese subjects, respectively [9]. This finding is supported by other research on bodybuilders, showing that BIA underestimated fat mass, and overestimated fat-free mass when compared to the four-compartment model [10]. Research conducted by Jebb et al. (2000) [11] found that leg-to-leg BIA using the manufacturer equations resulted in large errors when attempting to predict body fat, relative to the four-compartment model. The authors subsequently developed a novel prediction equation to estimate fat mass from the same Tanita bioimpedance analyser, with the four-compartment method as a reference. However, later research found that this equation also failed to outperform the Tanita manufacturer equation, and resulted in wide limits of agreement [12].

Potentially of greater concern to practitioners considering the use of BIA to determine body composition in the applied setting, are the individual error rates of BIA, rather than data on group means. The study mentioned previously on obese subjects [9] reported that in 12 of the 50 participants, BIA underestimated fat mass by 5 kg or more. This is supported by the findings of Van Marken Lichtenbelt et al., [10], who reported an 8 % individual error rate when comparing BIA with the four-compartment model. This suggests that BIA may provide data that is not sufficiently accurate for the determination of individual body composition.

The validity of using BIA to measure changes over time
A further consideration for the use of BIA is the validity of its use in measuring changes in fat mass and fat-free mass over time, as this may indicate the efficacy of a nutritional or training intervention looking to manipulate body composition. To revisit the study by Ritz et al. (2007) [9], BIA was unable to accurately assess changes in body composition when compared to the four-compartment model. Fat mass was underestimated by 1.6 kg, whereas fat-free mass was overestimated by 1.8 kg. Individual error rates were greater than at baseline, with BIA underestimating fat mass by 7.5 kg in some subjects.

A further study on obese populations [13] showed individual disagreement in body fat measurement between BIA and the four-compartment model was high. Individual measures of body fat ranged from -3.6 % to 4.8 % of the four-compartment value, highlighting the potential for significant discrepancies when measuring individual body composition over time. BIA is likely to misrepresent changes over time, potentially missing significant changes in body composition, or suggesting changes that haven’t occurred.

There are a limited amount of comparisons between BIA and the reference four-compartment model in athletic populations. There is disagreement amongst the limited research available, with only one study suggesting that BIA is suitable for assessing body composition in athletes [15], whereas other research suggests that body fat estimates are much higher in athletes when using the BIA method [16].

The discrepancies between the studies may be due to various issues including differences in methodology, equations, and athletic population. There are currently no BIA equations for athletes that have been derived from the criterion four-compartment method (fat mass, total body water, bone mineral mass, residual mass). This makes the application of BIA in this population difficult, as athletes are likely to possess substantially different quantities of fat and fat-free mass when compared to the general population or diseased populations that current equations are based on.

The reliability of BIA
The reliability of BIA (the reproducibility of the observed value when the measurement is repeated) is also important to determine single-measurement precision, as well as the ability to track changes over time. A plethora of research has indicated the importance – and potentially the inability – of standardising BIA measures to sufficiently account for various confounders.

The mean coefficient of variation for within-day, intra-individual measurements, has ranged from 0.3 % to 2.8 %, with daily or weekly variability ranging from 0.9 % to 3.6 %, respectively [2, 17]. Standard measurement conditions may vary depending on the machine type (e.g. hand-to-hand, leg-to-leg, supine vs. standing, etc.). Other factors which may impact the BIA measurement and should therefore also be standardised are [16]:

• Room temperature
• Placement of electrodes
• Preparation of the skin
• Hydration status
• The analyser itself

The standardisation of hydration status is clearly of importance for BIA, as the method is reliant on estimations of total body water to ascertain fat-free mass. For female athletes, difference in hydration status during menses may significantly alter impedance [17] and should be a consideration when assessing female athletes with BIA.

Saunders et al. (1998) [18] showed that BIA was not a suitable method of body composition assessment in athletes with abnormal hydration status (e.g. hyperhydrated or hypohydrated), indicating that even small changes in fluid balance that occur with endurance training may be interpreted as a change in body fat content.

In addition, eating and strenuous exercise 2-4 hours prior to assessment have also previously been shown to decrease impedance; ultimately affecting the accuracy of the measurement [19]. The need to standardise eating, exercise, and both acute and chronic hydration changes are clearly important to provide valid body composition estimations.

Are there issues with Bioelectrical Impedance Analysis?

As mentioned previously, there are several issues with BIA measurement that may limit its use in an applied setting. Methodological limitations of BIA may affect the ability of the method to accurately determine body composition. The primary issues with BIA are:

• Sensor Placement
• Hydration and Glycogen Levels
• Effect of incorrect measures in the applied setting
• Variations in manufacturers’ equations

Sensor Placement
One such limitation is the placement of the sensors, and their ability to give readings of total body composition. As electrical current follows the path of least resistance, some scales may send current through the lower body only, missing the upper body entirely. Similarly, hand-held instruments may only assess the body composition of the upper extremities.

As females typically have a higher proportion of adipose tissue in the gluteal-femoral region [20], it is possible that this would not be represented using hand-held BIA devices. Hand-to-foot BIA devices, however, may allow for greater accuracy, as the current is sent from the upper body to the lower body, and is less likely to be influenced by the distribution of body fat.

Hydration and Glycogen Levels
Regardless, all devices are still subject to the same limitations that other BIA devices are. The assumption that the hydration fraction of skeletal muscle remains at 73 % is based on the chemical analysis of six cadavers as part of the Brussels Cadaver Analysis Study [21]. However, hydration of fat-free mass has been shown to rise to over 77 % with increased levels of body fat [22].

Deurenberg et al. (1988) [23] reported an underestimation of fat-free mass when assessing changes in body composition following weight loss. They speculated that changes in glycogen stores, and the loss of water bound to glycogen molecules, may affect BIA estimates of fat-free mass. In athletic populations, where varying glycogen stores are likely throughout a training week, it is likely that this will lead to some variation in the detection of change in fat-free mass in athletes as glycogen is likely to be affected by both diet, as well as the intensity, duration, and modality of previous training sessions – even with protocol standardisation.

Effect of incorrect measures in the applied setting
An important consideration when assessing the individual variation of BIA is the potential consequences that an incorrect reading can have. As reported earlier, error rates can range from 4-8 % for individuals, and, therefore, an athlete could have decreased body fat percentage by 4 %, whereas their BIA would report that they had gained 4 % body fat. This can have wide-ranging implications, from assessing the efficacy of previous dietary and training interventions to making decisions on the correct interventions moving forward.

For example, an athlete may be singled out for interventions to reduce their body fat based on their BIA assessment and normative values, yet other methods may suggest that their body composition is optimal. Without considered interpretation of this result, coaches may question an athlete’s commitment and professionalism if they believe that their body fat has increased drastically over time. Similarly, athletes often take interest in their body fat percentages, and a false score indicating so-called “negative” changes in body fat may impact the confidence and compliance of an athlete.

Is future research needed with Bioelectrical Impedance Analysis?

The primary area for future research in this area is clearly the need for validated BIA equations for athletes in a range of sports and with varying body composition. It is important that these equations are validated using a total-body, water-based, four-compartment method, in an attempt to minimise the measurement error that is found when equations are based on the two-compartment model; such as hydrostatic weighing. As such, the following areas of research are needed to expand current knowledge on this topic:

• A BIA equation for use in athletic populations
• The validity of BIA vs. the four-compartment model for athletes with “more extreme” body composition (e.g. greater muscle mass, leaner, etc.)
• The ability of BIA to accurately detect changes in athletes’ body composition over a period of time

Conclusion

To conclude, it is likely that BIA is not a suitable body composition assessment method for athletic populations. The lack of a validated equation for this population, combined with the large individual error reported in overweight and obese populations, suggests that BIA does not provide accurate body composition data for both single-measure and repeated measures.

The post Bioelectrical Impedance Analysis (BIA) appeared first on Science for Sport.

1. Summary
2. What is a DEXA scan?
3. How does DEXA scanning determine body composition?
4. Are DEXA scans valid and reliable?
5. Are there issues with DEXA scans?
6. Is future research needed with DEXA scans?
7. Conclusion
8. References
9. About the Author

Summary

Dual-energy X-ray Absorptiometry (DEXA) scans determine body composition by passing low-radiation X-ray beams through tissues, with varying energy absorption allowing the practitioner to determine both tissue type and quantity. DEXA scans are the gold standard method for measuring bone density and are used for diagnosing bone conditions in clinical and sports settings.

Despite the potential for individual error, DEXA scans appear to be a valid measure when comparing mean results against the criterion four-compartment method (fat mass, total body water, bone mineral mass, residual mass). The reliability of DEXA scans can vary, however, the error can be minimised by standardising the measurement conditions and equipment, in addition to following best practice.

Current research suggests that DEXA scanning can be a valuable assessment tool for practitioners intending to measure body composition (e.g. levels of body fat).

What is a DEXA scan?

Dual-energy X-ray absorptiometry (DEXA) is a measurement technique used to determine bone mineral density. DEXA is increasingly being used to determine body composition in a variety of settings, including obesity [1], sarcopenia [2], and elite sport [3].

A DEXA scan involves the passing of low-radiation X-ray beams through bones, and the amount of energy that is absorbed allows for the determination of the density of the bone. Radiation energy per pixel is established and converted to g/cm for a given area. The number of pixels in each area is determined, and the amount of bone in each pixel is calculated, allowing for the calculation of bone density in specific bones [4].

Two energies are used to estimate soft tissue absorption, separate from the bone. The DEXA scan creates a two-dimensional image, preventing the measure of bone depth.

How does DEXA scanning determine body composition?

There are several methods used to determine body composition, and each method has pros and cons regarding the accuracy, financial cost, practicality, and other factors. DEXA scans provide a 3-compartmental model for body composition estimation. The three compartments are;

1. Bone mineral
2. Fat
3. Other fat-free mass that isn’t bone

A criticism of 2-compartment models (dividing weight into fat mass and fat-free mass) is that errors occur due to assumptions of the density in fat mass and fat-free mass (e.g. variations in bone density have been identified between different ethnicities [5]). This, therefore, demonstrates a potential advantage of DEXA scan use.

DEXA scanning offers a number of potential benefits for body composition assessment. The scan itself involves the movement of a ‘machine arm’ across the length of the body being measured. A full body scan takes approximately eight minutes, making it a time-efficient, non-invasive measure of body composition. The scanner can also be adjusted to measure a single body part or segment, providing regional estimations of body composition.

DEXA scans are used to determine bone mineral density. DEXA scanning is considered the gold standard method for determining bone mineral density and is thus used in the assessment of osteopenia (lower than average bone density) and osteoporosis (much lower bone density, risk of bone fracture). This can be important in certain athletic populations with a greater risk of low bone density and osteoporosis, such as long-distance runners (predominantly females).

Therefore, the use of serial DEXA body composition measurement may provide data regarding athlete conditioning, training outcomes, and rehabilitation progress, as well as indicate increases in fat mass, or decreases in skeletal muscle mass or bone mineral density that could be detrimental to athletic performance [6].

Are DEXA scans valid and reliable?

The validity (the agreement between the true value and a measurement value) of body composition is key to determining the precision of a measurement. A multi-compartment model, specifically the four-compartment model (fat mass, total body water, bone mineral mass, residual mass), is regarded as the criterion method in determining body composition, and thus this model is often used in comparison when assessing the validity of DEXA measurements [7]. Despite this, there is a relative lack of validation studies using these comparisons, predominantly due to the time, labour, and financial cost involved [7].

The validity of DEXA scans when compared to the four-compartment model is currently equivocal. A study by Arngrimsson et al. (2000) in male and female distance runners reported that DEXA underestimated body fat measurements by around 2 % [8]. This finding was replicated in a study by Van der Ploeg et al., (2003) in a healthy adult population [9], and slightly less than the 4 % difference in trained men found by Withers et al. (1998) [10]. Fat-free mass was also reported to be overestimated by approximately 2.5 kg [10].

Conversely, other research has shown an overestimation of body fat by 3-4 %, and an underestimation of fat-free mass by up to 3 kg [3, 11]. The differences in the reported validity of DEXA scans for body composition may partly be due to differences in software, manufacturer, and the wide range of body composition values across various cohorts. It is worth noting that although current research is conflicting, it is commonly assumed that DEXA is a valid technique for the assessment of body composition.

It is also important to consider the validity of DEXA results not only for cross-sectional data (observational data at one given time point) but also when assessing changes in body composition over time, as this is often of interest to the sports science practitioner. Early research in this area used the novel method of scanning participants, before placing lard samples on the participants to simulate a gain in adipose tissue. One study showed that fat mass and lean body mass measurements were not significantly different from the expected change following the placement of 8.8 kg of lard on healthy female subjects [11]. Another study also reported measurements in line with expectations when 11.1kg was added, however, there was evidence that fat mass was underestimated by 1.9 % when the amount of lard added was increased to 22.2 kg [12].

Data on DEXA use to assess body composition in athletic populations is scarce. Van Marken et al., [13] showed no difference in mean body composition change between the four-compartment model and DEXA scans in male bodybuilders. This finding was replicated by Santos et al., [3] in male judo competitors. Despite these findings, both studies reported that individual error rates of 4% for body fat measurement, highlighting the large individual differences within measurements.

The reliability (the reproducibility of the observed value when the measurement is repeated) of DEXA scanning is important for determining the precision of a single measurement, as well as the ability to detect change over multiple measurements. Despite the various benefits of DEXA scanning for body composition assessment, it is not without error. Inconsistent results can occur when using scanners from different manufacturers [14], as well as two scanners produced by the same manufacturer [15]. Software updates may also alter body composition results due to algorithm variation [16]. Differences in the x-ray beam used can also contribute to error; pencil beams and fan beam DEXA systems have been shown to provide significantly different body composition test results, limiting comparison between the two [17].

Biological variation may also affect reliability in DEXA measurements, particularly in athletic populations. The effects of fluid intake on body composition have been studied previously, showing that fluid intake of 0.8-2.4 L of water can significantly increase lean mass estimates in the trunk region [18]. Similar results were reported by Thomsen et al. (1998) [19], showing that estimates of lean body mass were increased by over 1 kg per hour following a standard meal weighing 1311 g, as well as after drinking 1 L of water.

Changes in any of these variables are not uncommon in athletic populations and may mask or give the false impression of changes in skeletal muscle mass. Bone et al. (2016) [20] reported that glycogen and creatine loading increased lean mass values by 2.1 % and 1.3 %, respectively. A variation in hydration status of 5 % has also been shown to alter DEXA predictions of body fat by 3 % [21]. As manipulation of these variables is commonplace with athletes, standardisation is key to accurately detect meaningful change.

Are there issues with DEXA scans?

Practically, DEXA scanning may also have some issues. The cost and accessibility of DEXA scanning can be an issue, particularly for sports teams that have large squad numbers and limited budgets. Furthermore, DEXA scans do use ionizing radiation, and although the absolute dose per scan is minimal, it may be advisable to minimise the number of scans performed per year on one individual; limiting the quantity of body composition data that can be acquired [22]. In these instances, it may be prudent to use more basic, cost-effective methods of body composition assessment such as skinfold calipers.

A further issue (particularly with some athletic populations) can be the size of DEXA scanners, as they may not be able to accommodate individuals who are taller or broader than the scanning area. For tall athletes, one technique is allowing the athlete to bend their knees, thus allowing the head and feet to be included in the scan. However, this has been shown to lead to significant error, with fat mass (9.2 %) and lean body mass (4 %) both vastly overstated when compared to a standard measure [23]. Other methods involve the summation of separate scans to achieve a total body measurement, however, this method also leads to substantial error [24].

As mentioned previously, reliability may be an issue if best practice is not observed, and measurements are not correctly standardised [25]. Measurements of fat and fat-free tissue are also made on the assumption that fat-free mass hydration is constant at 73 % [26], however, the hydration of fat-free mass has been suggested to be 72-74.5 % [27]. Although not necessarily an issue, as all measurement methods have an inherent error due to assumptions, these variations may potentially cause detectable variability in the estimation of fat-free mass.

The Best Practice Protocol for the assessment of whole body composition by DEXA

A standardised protocol for the use of DEXA to measure body composition of athletes is necessary, but there is an absence of a universal approach to this need. Nana et al. (2014) published a best practice protocol of DEXA body composition assessment suitable for use in a real-life athlete setting – with known reliability which has been optimised in terms of the balance between the effort required to achieve it and the benefits of its precision [30]. This protocol, presented below, was developed from first principles, in conjunction with pilot work and a series of studies of sources of measurement variability.

Although this protocol ensures maximum precision, it poses some practical costs and the burden on both the athletes and the technician that need to be balanced against the potential value of the additional precision gained. As an example, if a player turns up late and the technicians rush through the calibration of the scan, or the athlete is not aligned in the scanning area correctly,  it can skew the results.

Is future research needed with DEXA scans?

In terms of body composition assessment, a greater dataset of reference body composition data for athletes in various sports (and various positions/roles within these sports) is needed. To this end, the ability of DEXA scans to also provide segmental analysis may provide practitioners to identify ‘regions of interest’ that may determine successful performance in these sports or roles. This also may allow greater insight into the effects of an intervention that has been utilised. For example, if lower-body musculature is identified as important, the ability to track changes in skeletal muscle at this site could be useful.

Furthermore, the Best Practice Protocol should be considered a recommended standard until future work allows further refinements to be made. Furthermore, studies which involve DEXA measurements of body composition should report details of how their scanning techniques conform to this or other standardisation procedures.

Conclusion

Despite the inherent limitations of body composition assessment via DEXA scan, with considered application and interpretation in the right context it can provide a robust and suitably reliable assessment of body composition, particularly in comparison to other body composition assessment methods.

The ability of DEXA scans to provide greater levels of detail regarding bone density and segmentation measurements makes it an attractive choice to practitioners who have the logistic and financial means.

The post DEXA Scans appeared first on Science for Sport.

1. Summary
2. What does body composition mean?
3. What is body composition testing?
4. How is body composition measured?
5. Are there any issues with body composition testing?
6. Is future research needed with body composition?
7. Conclusion
8. References
9. About the Author

Summary

Body composition is an area of interest in the fields of both health and sporting performance. In health, body composition has long been of interest, potentially more so with the excessive fat mass evident in obese populations, and the limited skeletal muscle mass in the elderly. In athletic performance, changes in body composition such as reduced fat mass and increased fat-free mass are often highlighted as determinants of successful performance, and the target of multiple interventions.

Over the years, several methods of body composition measurement have been suggested and used, each method likely to have application in certain scenarios, with a trade-off usually occurring between accuracy and reliability, and cost and practicality.

What does body composition mean?

The term body composition relates to the proportion of the body that is made up of fat mass (FM) and fat-free mass (FFM) (1).  In 1992, Wang et al. (1992) (2) proposed a five-level model for body composition research:

1. Atomic level
2. Molecular level
3. Cellular level
4. Tissue-organ level
5. Whole-body level

Each level has different compartments and can be separated into two, three or four compartments. How these compartments are measured and/or estimated varies, and will be elaborated on later in this article.

What is body composition testing?

Body composition provides objective data that is unobtainable from scale weight alone, such as the proportion of weight that is fat mass. For example, a coach may single out an individual as overweight based on scale weight or BMI. However, being heavier may be an advantage in some sports, if a greater proportion of the weight is skeletal muscle mass (e.g. a forward in rugby union). Body composition may also have application in weight-making sports, to determine the respective loss of FM and FFM during weight-cutting for a competition (3).

In runners, an emphasis is placed on the power-to-weight ratio, and optimising body composition is likely to be beneficial when attempting to improve this determinant of performance.

On the flip side of that, body composition can be used to determine whether an athlete is fuelling sufficiently for performance. Research on the Female Athlete Triad has shown that low energy availability (as determined using kg of FFM) can have negative impacts on several health markers, such as bone density (4).

It is clear that body composition can provide valuable data for a number of athletes. An objective measure of body composition at baseline, and then at subsequent time points, can be helpful when determining an individual’s response to an intervention that has been put in place (e.g. a new diet plan). This is important as objective data clearly shows the efficacy of an intervention, if checked regularly, can allow for changes to be made.

How is body composition measured?

Body composition can be assessed in a number of different ways, with methods ranging in technicality, cost and accuracy. The only fully accurate measurement of body composition is cadaver whole-body dissection analysis, previously undertaken as part of the ‘Brussels Cadaver Analysis Study’ (5). In light of this, there are no in vivo techniques for body composition analysis that will provide complete accuracy, and so body composition assessment is an estimate, often made on assumptions regarding the proportions and properties of FM, FFM, water, protein, and other minerals.

Methods that are likely more accessible to the general public include skinfold calipers and bioelectrical impedance analysis, whereas research laboratories and elite sports teams may have access to hydrostatic weighing, DEXA, and Whole Body Plethysmography (BodPod). Each method has its pros and cons, and it is likely that there is no one technique that is optimal for all situations – this will be discussed in the following sections.

Dual-Energy X-Ray Absorptiometry (DEXA)
DEXA is based on a three-compartment model that measures bone mineral content, FM and FFM. A full body DEXA involves the individual lying on an open scanner for approximately eight minutes, whilst the ‘arm’ of the machine moves over the length of the body, and scans using two x-ray beams; the more energy that is absorbed, the denser the tissue. Two energies are used to allow estimates of soft tissue absorption, separate from bone (6).

DEXA is considered the gold standard measurement tool for the diagnosis of osteopenia and osteoporosis (7). It is fast, non-invasive, and only exposes individuals to a small amount of radiation. The DXA also has the added benefit of providing segmental body composition analysis, which may be of particular interest when looking at bone mineral density in some athletic populations.

Although it is often considered one of the most accurate methods of body composition analysis, it is not without limitations. In athletic populations, longitudinal data from repeated measurements of body composition may be affected by muscle glycogen levels, hydration status and changes in muscle metabolites such as creatine (8). This may lead to a misrepresentation of FFM, and these factors should be considered when interpreting the results of DXA estimates of body composition.

Skinfold Calipers
Skinfold calipers measure a double fold of skin and subcutaneous adipose tissue and apply constant pressure to the site. Skinfold measurements make the assumption that adipose tissue compresses in a predictable manner, that the thickness of the skin is negligible, and the double-layer compression is representative of an uncompressed single layer of adipose tissue. Measurements give results in millimetres, which can be then converted to a body fat percentage, with dozens of equations available for varying populations.

A potential limitation of skinfold measurements is that they are dependent on the competency and accuracy of the person taking the measurements (i.e. intra-rater reliability). To minimise the technical error of measurement, measurement sites and techniques have previously been defined (9). Practitioners can become accredited in anthropometric measurement through the International Society for the Advancement of Kinanthropometry (ISAK).

To qualify, practitioners must assess various skinfold sites, girths and breadths, and report values within 10 % of a Level 4 anthropometrist, and within 7.5 % of their own values when repeating measurements. Skinfold measurements taken just one centimetre away from the defined ISAK sites have previously been shown to produce significant differences in measurement values at each site, indicating how important it is to mark and measure skinfolds correctly for accurate data (10). Therefore, the intra-rater reliability of the test is extremely important.

Whole Body Plethysmography (BodPod)
Air-displacement plethysmography (ADP) can allow for the calculation of body composition through a 2-compartment model, based on assumptions of value constants for FM and FFM densities. ADP determines body volume by measuring the reduction in chamber volume caused by the introduction of a subject/athlete into a chamber with a fixed air volume (11). Body weight and body volume are determined by this method, with mass divided by volume providing a measure of density.

Again, this measurement is a non-invasive and quick method, with the advantage of not requiring exposure to radiation. BodPod has been shown to be a valid measure of group average body composition when compared to DEXA in female collegiate athletes (12). However, research has suggested a difference of 5.3 % between the BodPod and validated four-compartment models, with an error rate of 15 % (12). BodPod has also shown limited accuracy when attempting to determine changes over time (13), a primary consideration when choosing an assessment method for athletes.

Hydrodensitometry (Underwater Weighing)
Also known as ‘hydrostatic weighing’ or ‘underwater weighing’, hydrodensitometry works in a very similar way to the BodPod, in that it determines body volume, but this time based on how much water is displaced rather than air displacement. Body density is determined and then used to estimate body fat percentage. Again, this estimate is based on assumptions regarding the density of FFM, which may vary with age, gender, ethnicity and training status, potentially limiting its use in athletic populations (14).

Although this method was previously considered the gold standard by the American College of Sports Medicine, it is not without measurement error. The results are highly reliant on subject performance, and as the process itself is uncomfortable, it may take multiple tests to get a valid measurement. For example, measurement error can occur through unsuccessful attempts to blow all of the air out of the lungs, or air bubbles trapped in hair or swimsuits.

Bioelectrical Impedance Analysis (BIA)
BIA is based on the concept that electric current flows through the body at different rates depending on its composition (15). It involves running a light electrical current through the body and determining body composition through the resistance of the tissues to the electrical current. This provides a measure of total body water, which is converted to FFM on the assumption that 73 % of the body’s FFM is water (16). The low cost, speed of measurement and lack of need for technical expertise make BIA an attractive option for body composition measurement, particularly in epidemiological research. However, the accuracy of BIA is dependent on several factors, and as a result, it is likely the least valid measure of body composition discussed in this article.

BIA relies on empirical equations to estimate total body water, FFM and body cell mass; these equations use gender, age, weight, height and race as variables. Therefore, for BIA to be used as a valid measure of body composition, the correct equation must be used based on these factors (17). The validity of BIA in some populations has been questioned, particularly in obese patients (18), and in those with conditions that may alter fluid distribution, such as oedema (19).

BIA data is influenced by hydration status, and although the standardisation of fluid intake in the hours before testing may reduce the effects of hydration on body composition measurements, there is a lack of standardisation, or at least its reporting, in current research (20). This may also be true in applied practice with athletic populations. Exercise has been shown to lead to vast inaccuracies in body composition analysis using BIA (21), as has changes in hydration status (22). As a result, BIA may not be suitable for determining body composition changes due to fluctuations in hydration status following training.

Are there any issues with body composition testing?

Body composition measurement in vivo is an estimate. As mentioned previously, the cost and practicality of measuring body composition vary greatly, and often there is a trade-off between the two. Each method has a use in certain settings, and it is the job of the practitioner or user to understand the benefits and drawbacks of each method.

A further issue with body composition is often the focus of individuals on their absolute body fat percentage values. As highlighted previously, the variation in values can be significant, with differences in assessment method, the athlete’s physiological state and other assessment-specific variances affecting overall results. Rather than focusing on an absolute body fat number, it may be of more value to standardise measurement and track changes over time.

Is future research needed with body composition testing?

As measures of body composition are developed, more accurate measurements of FM and FFM can be established in various athletic populations. This means that future research should aim to determine:

• Body composition reference values for a larger range of sports, including positional differences in sports where this may be significantly different.
• How body composition may determine performance in these sports.
• Aspects of an athlete’s lifestyle that may impact the validity of body composition, and how to standardise these factors to improve measurement accuracy.

Conclusion

There are a multitude of body composition assessment tools available to the practitioner, each with varying cost, accessibility and accuracy in each population. It is important to understand the benefits and limitations of each method, and how best to utilise each one in practice. Most assessment tools are useful in various settings, and accuracy can be improved with proper standardisation prior to testing.

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