## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(B\) & \(\mathbf{SE}\) & \(\boldsymbol{p}\) & \(\boldsymbol{Z}\) & \multicolumn{2}{c}{**95\% CI**} \\ \cline{2-7}  & & & & & \(LL\) & \(\boldsymbol{UL}\) \\ \hline BCM T0 & 0.10 & 0.05 & **0.038 \({}^{\star}\)** & 2.08 & 0.01 & 0.19 \\ BI T0 & 0.00 & 0.01 & 0.916 & 0.11 & \(-0.03\) & 0.03 \\ sex & \(-0.23\) & 0.60 & 0.700 & \(-0.39\) & \(-1.41\) & 0.95 \\ Paresis on dominant arm & 1.02 & 0.51 & **0.046 \({}^{\star}\)** & 1.99 & 0.02 & 2.01 \\ Intercept & \(-4.80\) & 2.01 & **0.017 \({}^{\star}\)** & \(-2.01\) & \(-8.74\) & \(-0.87\) \\ \hline \hline \end{tabular} B: estimate; SE: standard error; CI: confidence interval; LL: lower limit; UL: upper limit t; BCM T0: body cell mass at baseline; BI T0: Barthel Index at baseline; \({}^{\star}\)\(p<0.05\).  
\end{table} 
Table 5: Multiple logistic regression analysis for recovery in ADL. From our results, besides the above-discussed BIA parameters, BCM also appears to play a role in the evaluation of post-stroke recovery. BCM had the higher correlation with the baseline ADL. Moreover, higher values of BCM and BCMI at admission were seen in patients with a better recovery. These results could be connected with the known link between BCM and nutritional status. BCM evaluation identifies the metabolically active part of FFM, in contrast to bone and extracellular water. Hence, it reflects the body's cellular components involved in oxygen consumption and resting metabolism [22; 32]. BCM is associated with the resting energy expenditure (REE), which represents energy consumption to maintain essential life functions while at rest [32]. It has also been suggested that measuring BCM is important for the nutritional screening and management of critically ill patients. In fact, low values of BCM reflect a malnutrition status for such serious subjects [33]. As found in our logistic regression analysis, BCM was independently associated with better recovery of post-stroke patients. Notably, the performance in ADL at baseline, sex, and paresis in the dominant arm did not influence this association. The sample size of this study was insufficient to verify the effect of more confounders as independent variables in the regression analysis. We are planning to verify if BCM may effectively predict the recovery in post-stroke patients on a larger sample. Another result is that, in our study, we found no differences in patients' body composition parameters after six weeks of rehabilitation treatment. The literature on this topic is very poor, and in disagreement. One study reported that muscle mass did not differ in post-stroke patients after a four weeks of rehabilitation treatment [43], while another one found a loss of lean and fat mass and an increase in muscle mass after a five-week rehabilitation program [44]. In addition, this study showed that in post-stroke patients, SMI increased with rehabilitation of 1-6 months and that the increase was independently associated with length of hospital stays [45]. In our opinion, the intervention time is too short to observe a positive change in body composition. In fact, elderly patients with strokes may suffer from a decline in muscle mass related to aging and immobility [12], and they should be treated with a specific rehabilitation protocol that considers adequate nutritional intervention and exercise training. In fact, it has been shown that a specific exercise training program [46] together with adequate supplementation [47] could ameliorate the body composition of post-stroke patients. The preliminary results presented herein deserve future study on a bigger sample in order to confirm the data. In fact, one limitation of this study is due to a limited sample recruited only in a single rehabilitation facility. A multicentered study planned in the near future will deal with this restriction and will increase the sample size.