*3.2. Skinfold Thickness Assessment*

Despite the aforementioned simplicity of skinfold measurement, which makes this method popular within applied settings, there are a number of technical limitations that must be considered when employing this technique. Initially, there is an assumption of constant skin thickness and compressibility in the double fold between differing intra and inter individual measurement sites. This is also affected by the grip of the practitioner and the applied pressure of the caliper, and an athlete's age, sex and skin temperature. However, skinfold assessment has also been shown to be the least affected method by everyday activities, ingestion of a meal and changes in hydration status [79,91]. To that end, the experience of the anthropometrist is crucial in obtaining accurate skinfold data. From our own experiences and previous reports, as little as a 1 cm difference in the assessed measurement site can have significant effects on the outcome data [92]. Even with ISAKaccredited practitioners, it is not uncommon to see large discrepancies in assessment outcomes, particularly in larger athletes, which can create issues when multiple testers perform the measurements across groups of individuals.

A key issue when utilising skinfold thickness in the applied setting is the desire for FM measurements to be reported as a BF%, which adds another layer of complexity and turns an indirect method into doubly indirect. Doubly-indirect methods incorporate regression equations by plotting results against a criterion standard to create an estimate of composition. The conundrum with these regression equations is there are currently over one hundred such formulae for the estimation of BF% from skinfold thickness measurements alone [73], and these equations have not ye<sup>t</sup> been validated when tracking regular changes in body composition [93]. These formulae are also established across varying populations, using numerous protocols, with deviations in sites measured and often have intra-practitioner and criterion variability and reliability issues. This can be characterised by the use of different equations on the same set of individual data producing resounding differences (as highlighted in Table 2), where data on a Caucasian male soccer player resulted in ranges between 4 and 8% body fat dependent upon the equation used. Therefore, the conversion of skinfold thickness into a BF% should be discouraged with data presented as a sum of the 8 skinfold sites providing a more accurate and reliable outcome of body composition assessment [20,94,95]. Indeed, the sum of skinfold thicknesses has a high degree of agreemen<sup>t</sup> with whole-body measures from DXA [96]. However, there are some considerations with this approach. Firstly, it is not possible to further estimate FFM, which is often useful information for those in the field. The second problem is that many coaches are not familiar with being given data as a 'sum of mm' and often still request relativiseddata. This is compounded by limited normative data of 8 skinfold measures in athletes and thus coaches can be somewhat confused when presented with such information. We therefore present a set of normative data taken from applied practitioners working in the field, which we believe can help to address this problem (Table 5). Finally, even in ISAK-accredited practitioners, it is not uncommon to see sum of 4, 7 or 8 sites reported. This can cause confusion and make it difficult to compare data. If the field is to move away from percentage values then it is important that a widely accepted methodology is adopted, which we would sugges<sup>t</sup> is the standard ISAK sum of 8 sites as presented in Table 4.

**Table 5.** Overview of Σ8 skinfold ranges (mm) in a variety of sports (data compiled from personal communications with peers working in elite performance). Lower, middle and upper ranges suggested are based upon typical values measured in elite sport although it must be stressed that attributing performance to skinfold measures is difficult to establish.



**Table 5.** *Cont.*

#### **4. Conclusions and Recommendations for the Field**

Despite the assessment of body composition being routine practice in applied sports settings, there would appear to be nothing routine with regards to the techniques used to assess it. All of the methods discussed in this review have strengths and weaknesses, and at times may be deemed 'best practice' in specific athletic situations and to address specific questions. A schematic representation of the pertinent considerations that could be addressed when making a decision on the preferred method of assessing body composition can be seen in Figure 2. We would sugges<sup>t</sup> where BMC needs to be examined, or when it is necessary to take limb-specific estimations of FM and FFM, then DXA appears to be the assessment tool of choice (providing the pre-scan conditions can be controlled as discussed). However, given the simplicity of the skinfold technique, the speed in which it can be implemented and assessed, the frequency of which it can be used along with the low costs associated with the method. If the goal is to simply track changes in body fatness over time, it could be argued that skinfold measures may still provide the best solution when reported as a sum of mm rather than a relative percentage value. Combined with the fact that of all of the assessments of body composition, skinfold assessments appear to be the least affected by factors that are difficult to control in athletes (food intake, hydration status, daily activity) perhaps it is now time to say in applied sports practice 'come back skinfolds, all is forgiven'.

**Figure 2.** A proposed body composition method decision-making tree. Evidence base and applicability of all methods should be considered within the specific context in which they are being applied, be conducted by a suitably accredited/trained individual with all risks managed and should deliberate all points made throughout the current article prior to application of the chosen method. Green arrows indicate yes as the answer, red arrows indicate no as the answer, and black arrows indicate the potential flow of questioning when considering different methods of anthropometrical assessment.

**Author Contributions:** Manuscript prepared and reviewed by full authorship. All authors have read and agreed to the published version of the manuscript.

**Funding:** This review received no external funding.

**Institutional Review Board Statement:** All data used within this review were sourced from published peer-reviewed literature or from unpublished research data from Liverpool John Moores University.

**Informed Consent Statement:** All published and unpublished data have informed consent from all subjects involved in this study.

**Data Availability Statement:** No application.

**Conflicts of Interest:** The authors declare no conflict of interest.
