# Optimizing Field Body Fat Percentage Assessment in Professional Soccer Players
## 4 Discussion  
This study sought to identify, using DXA as reference, which field method out of those based on anthropometric equations, absolute SKFs, and BIA was the most valid to predict %BF in elite soccer players. Our main findings were that (1) while all the anthropometric equations but one showed high correlation with DXA, only the equation developed by [PERSON] et al. (2018), did not show significant differences against DXA; (2) several sets of SKF measurements showed high correlation with DXA, especially the sum of four measurements (triceps, subscapular, supraspinale, and abdominal); and (3) compared to the anthropometry-derived data, BIA results correlated less with DXA measurements and significant differences in %BF were produced between BIA and DXA. Our data indicate significant standardized differences in fat mass prediction depending on the method used. DXA-derived body fat percentages were similar to values reported in recent studies conducted in international-level elite male soccer players from the Italian Serie A league [5] and the Australian A-league competition [1]. However, our DXA-derived values were higher than those described in Italian Series A league [3] and English Premier league [42; 43] players. These differences between studies could be attributed to possible differences in the training history of players [43], or the league stage [3], or perhaps even the different DXA model used and its software. All SKF-based anthropometry equations used in this study showed excellent correlation with DXA, yet variations in bias and agreement levels determine that these methods are not interchangeable. Moreover, we should be cautious when using them for fat mass evaluation in professional soccer players. Similar findings were found in international [5] and youth [2] male soccer players. There seems to be no agreement as to which SKF equations correlate best with DXA in the few studies that have compared these methods to predict fat mass in professional soccer players [5; 6; 7]. Two equations have been developed to assess %BF specifically in professional soccer players [5; 7]. The equation by [PERSON] et al. (2009), showed significant differences with DXA-derived %BF in our study, in line with the results of [PERSON] et al. (2018) and [PERSON] et al. (2018). In contrast, the equation of [PERSON] et al. (2018), showed good agreement with our DXA results, consistent with higher correlation with DXA reported than the equation of [PERSON] et al. (2009). The equation by [PERSON] et al. (2018), thus seems an effective way to estimate %BF based on two SKF measurements. However, this good performance needs to be confirmed in elite soccer players. The sum of several SKF measurements at different body sites showed high correlation with DXA, in agreement with the findings of three studies that used this method to calculate body fat in elite soccer players [2; 5; 36]. We found that the sum of triceps, subscapular, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Skinfold** & **Correlation** & **Significance (\(p\))** \\ \hline Triceps (T) & 0.81 & \textless{}0.001 \\ Subscapular (Sb) & 0.42 & 0.061 \\ Biecps (B) & 0.51 & 0.017 \\ Iliac crest (IC) & 0.75 & \textless{}0.001 \\ Supraspanale (Sp) & 0.76 & \textless{}0.001 \\ Abdominal (Ab) & 0.74 & \textless{}0.001 \\ Thigh (Th) & 0.69 & 0.001 \\ Calf (Ca) & 0.50 & 0.022 \\ Sum of 4 (T, Ab, Th, Ca) & 0.78 & \textless{}0.001 \\ Sum of 4 (T, Sb, B, IC) & 0.80 & \textless{}0.001 \\ Sum of 4 (T, Sb, Sp, Ab) & 0.82 & \textless{}0.001 \\ Sum of 5 (T, Sb, B, IC, Th) & 0.81 & \textless{}0.001 \\ Sum of 6 (Sb, T, Sp, Ab, Th, Ca) & 0.79 & \textless{}0.001 \\ Sum of 7 (B, Sb, T, Sp, Ab, Th, Ca) & 0.78 & \textless{}0.001 \\ Sum of 8 (B, Sb, T, IC, Sp, Ab, Th, Ca) & 0.81 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relationship between DXA-derived % body fat values and those derived from different skinfold measurements or the sum of several skinfold measurements in elite male soccer players (\(n=21\)).