# Wobble Board Performance: A Practical and Useful Quantification in Balance Assessment
## 3 Results  
Analysis of the correlation between WB and CoP variables during different measurement sessions derived coefficients r of \(-\)0.810 \(\pm\) 0.027 for sway area, \(-\)0.776 \(\pm\) 0.057 for anterior-posterior sway, \(-\)0.782 \(\pm\) 0.059 for mediolateral sway, and \(-\)0.621 \(\pm\) 0.050 for sway velocity across sessions (Figure 1).  
Descriptive statistics of WB performance and CoP variables derived from both the force plate and the WB-based polynomial regression predictions are presented in Table 1. The table also contains the correlation between WB performance and force plate CoP variables. Figure 2 displays the linear (1 r\({}_{\text{min}}\) 1 = 0.575-0.842) and nonlinear (1 r\({}_{\text{non}}\) 1 = 0.728-0.910) relationship between WB performance and force plate CoP variables. Table 2 displays the R\({}^{2}\) and adjusted R\({}^{2}\) results for all polynomial-transformed regression predictions, with the highest adjusted R\({}^{2}\) for polynomial transformation found in the f  
Figure 1: Correlation coefficients r between wobble board (WB) performance and force-plate-derived center of pressure variables during different measurement sessions.  
sway), sixth (mediolateral sway), and fourth order (swap area and velocity) at 0.798, 0.728, 0.735, and 0.557, respectively. The respective polynomial equations are included in Figure 2. The CCC (0.893, 0.685, 0.878, 0.778), absolute RMSE (0.09 mm, 0.13 mm, 0.16 cm\({}^{2}\), 0.07 mm\(\cdot\)s\({}^{-1}\)), and relative RMSE (6.5%, 12.1%, 10.7%, 5.2%) were documented between the transformed WB performance and anterior-posterior sway, mediolateral sway, sway area, and mean sway velocity, respectively. The transformed WB performance did not differ from the force plate anterior-posterior sway (d = 0.23, \(p\) = 0.439), sway area (d = 0.01, \(p\) = 0.984), and mean sway velocity (d = 0.02, \(p\) = 0.946) but differed from mediolateral sway (d = 1.01, \(p\) = 0.005).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **WB [\%]** & **AP [mm]** & **ML [mm]** & **Area [cm\({}^{2}\)]** & **V [mm\(\cdot\)s\({}^{-1}\)]** \\ \hline \multirow{3}{*}{mean \(\pm\) SD} & \multirow{3}{*}{36.58 \(\pm\) 22.09} & 7.30 \(\pm\) 1.91 \(\lx@math@degree\) & 5.96 \(\pm\) 1.11 \(\lx@math@degree\) & 8.43 \(\pm\) 3.41 \(\lx@math@degree\) & 64.20 \(\pm\) 9.26 \(\lx@math@degree\) \\  & & & 7.47 \(\pm\) 1.32 \(\lx@math@degree\) & 6.56 \(\pm\) 0.80 \(\lx@math@degree\) & 8.43 \(\pm\) 2.74 \(\lx@math@degree\) & 64.08 \(\pm\) 6.06 \(\lx@math@degree\) \\ \multirow{3}{*}{95\% CI} & \multirow{2}{*}{23.08–48.08} & 6.22–8.38 \(\lx@math@degree\) & 5.33–6.59 \(\lx@math@degree\) & 6.50–10.36 \(\lx@math@degree\) & 58.96–69.44 \(\lx@math@degree\) \\  & & & 6.73–8.21 \(\lx@math@degree\) & 6.11–7.01 \(\lx@math@degree\) & 6.88–9.98 \(\lx@math@degree\) & 60.65–67.51 \(\lx@math@degree\) \\ \hline \multirow{3}{*}{WB} & r & \multirow{3}{*}{1} & \(\mathbf{-0.842}\) & \(\mathbf{-0.714}\) & \(\mathbf{-0.795}\) & \(\mathbf{-0.575}\) \\  & & & \(<\)0.001 & 0.009 & 0.002 & 0.051 \\ \hline \multirow{3}{*}{AP} & r & \multirow{3}{*}{1} & \(\mathbf{0.836}\) & \(\mathbf{0.941}\) & 0.515 \\  & & & & \(<\)0.001 & \(<\)0.001 & 0.087 \\ \hline \multirow{3}{*}{ML} & r & \multirow{3}{*}{1} & \(\mathbf{0.969}\) & 0.500 \\  & & & & \(<\)0.001 & 0.098 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics as mean \(\pm\) standard deviation (SD) and 95% confidence intervals (95% CI) for wobble board performance (WB), anterior–posterior sway (AP), mediolateral sway (ML), sway area (Area), and mean sway velocity (V) as well as the linear correlation matrix.  
Figure 2: Scatterplots of wobble board (WB) performance and force-plate-derived center of pressure variables (i.e., anterior–posterior sway, mediolateral sway, sway area, and mean sway velocity) with correlation results expressing linear relationship (grey straight line; r\({}_{\text{lin}}\)) and nonlinear relationship via polynomial least square fit (black dotted curve; r\({}_{\text{non}}\)) as well as the polynomial regression equation.