## 4 Discussion
Oppositely, in the first 3 min segment of recovery, the RMSSD value was immediately increased with the opposite behavior of the sympathetic activity--a gradual increase during exercise and gradual withdrawal during recovery. Further, this is more plausible with the observed BF in the fifth 3 min segment in exercise as a negative HR predictor in the third and fourth 3 min segments in recovery, as the sympathetic tone was the highest during the last segment in exercise and the effects of its withdrawal were emphasized in the last segments of recovery. The new balance between the two autonomic branches might have resulted in the HR in the fourth 3 min segment in relaxation being a positive predictor of the HR in the last segment of recovery. Our results from the multiple regression analysis are correlated with the observation of autonomic behavior in post-exercise recovery in the study of [PERSON] et al. [9]. When it comes to RMSSD, the dominant overall predictor of parasympathetic activity in all five 3 min segments was BF, along with RMSSD in the relaxed state. The sudden rise in RMSSD in the first 3 min segment and somewhat settled RMSSD in the last 3 min segment of recovery were strongly predicted by RMSSD in third and fifth 3 min segments in the relaxed state, respectively (Table 9). In the early time segments of recovery, BF in early exercise was a positive predictor of RMSSD. The same pattern was observed for later segments--BF in the last time segments of exercise was a positive predictor of RMSSD in the late recovery. Once again, this suggests the fine tuning of the two autonomic branches according to the level of their activities in exercise and recovery. BF itself was strongly negatively predicted in the first and last 3 min segment of recovery by the level of RMSSD in the late time segments of exercise, while BF in the intermediate time segments was more predicted by the level of BF in the relaxed state (Table 10). These results are correlated with the observed upregulation of BF by the muscle afferents and \"central command\" in exercise and vice versa after the cessation of physical activity [12]. Finally, it is of note to mention that we found a strong negative correlation between the fat mass and parasympathetic activity during the recovery period and a positive correlation of the fat mass with BF and HR at the end of the recovery (Table 7). Our results are in line with the study by [PERSON] et al. [45], where a negative correlation was also observed between the fat mass and parasympathetic activity. Moreover, they observed a lower heart rate variability in obese people [45]. Also, the study by [PERSON] et al. demonstrated that the percentage of body fat significantly negatively correlated with heart rate recovery after exercise testing at the submaximal HR level [46]. This suggests that it takes longer for obese people to recover in comparison to the healthy population. However, the exact mechanisms of how obesity impairs cardiac autonomic modulation remain to be elucidated in further studies. In the end, we shall acknowledge a few limitations of our study. The sample size of ten participants is small, but we are convinced that the basic alterations in HR, BF, RMSSD, and ROC during the experimental protocol with a larger sample size study will have the same directions of change and preserved interrelationships. Despite being a pilot study, a larger sample size would enhance the robustness of our findings. In addition, including female participants could unveil intriguing gender-specific differences in the examined parameters. In an earlier study, we found sex differences between the nonlinear properties of cardiac signals and between the nonlinear properties of respiratory signals obtained in the supine position [25]. Hence, it could be expected that these are persistent in the form of cardio-respiratory coupling during exercise and recovery in healthy female participants.