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This research aims to describe and compare the effects of partial-body cryotherapy (PBC) and cold-water immersion (CWI) on the physiological responses of soccer players after cycling in a hot and humid environment. Sixteen elite soccer players participated in three experiments, and received CWI (13°C for 15 min), PBC (110°C−140°C for 3 min), and CON (room temperature: 21°C ± 2°C), respectively, after aerobic and anaerobic cycling in a hot and humid environment (temperature: 35°C–38°C; humidity: 60%–70%). Heart rate (HR), blood lactate (BLa-), perfusion index (PI), oxygen saturation (SaO2), core temperature (Tc), skin temperature (Ts), and rating of perceived exertion (RPE) were assessed at baseline and through 20 min (5-min intervals). HR was lower in CWI than CON after 20 min (p <.05). SaO2 was higher in CWI than PBC and CON between 10 and 20 min (p <.05). Tc was lower from CWI and PBC than CON between 10 and 20 min (p <.05). Ts was lower in PBC than CWI between 15 and 20 min (p <.05). RPE was lower in PBC than CON 20 min after the exercise (p <.05). No main group differences for BLa- and PI were observed. The physiological effects of PBC are generally similar to CWI. Compared with CON, both CWI and PBC could promote the recovery of physiological indexes within 20 min of exercise in a hot and humid environment. However, PBC can lead to a decrease in SaO2 due to excessive nitrogen inhalation. [ABSTRACT FROM AUTHOR]

Falls play a critical role in older people’s life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls. [ABSTRACT FROM AUTHOR]