BackgroundDeclined cognitive function interferes with dual-task walking ability and may result in falls in older adults with mild cognitive impairment (MCI). The mind-body exercise, Tai Chi (TC), improves cognition and dual-task ability. Exergaming is low-cost, safe, highly scalable, and feasible. Whether the effects of exergaming-based TC is beneficial than traditional TC has not been investigated yet.ObjectivesThe objective of this study was to investigate effects of exergaming-based TC on cognitive function and dual-task walking among older adults with MCI.MethodsFifty patients with MCI were randomly assigned to an exergaming-based TC (EXER-TC) group, a traditional TC (TC) group, or a control group. The EXER-TC and TC groups received 36 training sessions (three, 50-min sessions per week) during a 12-week period. The control group received no intervention and were instructed to maintain their usual daily physical activities. The outcome variables measured included those related to cognitive function, dual-task cost (DTC), and gait performance.ResultsThe EXER-TC and TC groups performed better than the control group on the Chinese version of the Stroop Color and Word Test, the Trail Making Test Parts A and B, the one-back test, gait speed, and DTC of gait speed in cognitive dual-task conditions after training. However, there were no significant differences between the EXER-TC and TC groups. Compared with the control group, only the EXER-TC group experienced beneficial effects for the Montreal Cognitive Assessment.ConclusionEXER-TC was comparable to traditional TC for enhancement of dual-task gait performance and executive function. These results suggested that the EXER-TC approach has potential therapeutic use in older adults with MCI.

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This study showed that beta ERD in the central area and postural control can be improved with balance training, along with lower extremity muscle strengthening exercise and turning-based treadmill training, in patients with PD. Furthermore, improvement in beta ERD in the central area correlated with improvements in postural control ability. This trial was registered at http://www.anzctr.org.au/ (ACTRN12616000198426).

Background: Far-infrared radiation (FIR) therapy improves vessel dilation, circulation, vessel endothelial function, and angiogenesis and reduces atherosclerosis. However, evidence of FIR therapy’s effects on foot circulation among diabetic patients undergoing hemodialysis is scarce. Aim: To determine whether FIR therapy improves foot circulation in diabetic patients undergoing hemodialysis. Design: Quasi-experimental. Methods: In June to November 2017, diabetic patients undergoing hemodialysis ( N = 58) at a hemodialysis center in northern Taiwan were divided into two groups: the experimental group ( n = 31) received FIR therapy to the bilateral dorsalis pedis artery (40 min/session, 3 times/week for 6 months) and the control group ( n = 27) received conventional dialysis care. Paired t test, independent samples t test, two-proportion Z test, and repeated-measures analysis of covariance were performed to compare changes from baseline to the end of the 6-month intervention between the groups. Results: Significant positive effects of FIR therapy on temperature, pulse, and blood flow of the dorsalis pedis artery were observed. Sensitivity to pain, tactility, and pressure also improved significantly in the experimental group. The Edinburgh Claudication Questionnaire revealed that the experimental group had reductions in subjective experiences of soreness, tingling, and coldness in the feet. Conclusion: The findings of significant improvements to objective and subjective measures of blood flow and neural function in the experimental group indicate that FIR therapy improves blood circulation to the feet. This therapy thus has great potential to be an effective adjuvant treatment for patients with diabetes mellitus undergoing hemodialysis.

This study developed a predictive model for cognitive degeneration in patients with Parkinson’s disease (PD) using a machine learning method. The clinical data, plasma biomarkers, and neuropsychological test results of patients with PD were collected and utilized as model predictors. Machine learning methods comprising support vector machines (SVMs) and principal component analysis (PCA) were applied to obtain a cognitive classification model. Using 32 comprehensive predictive parameters, the PCA-SVM classifier reached 92.3% accuracy and 0.929 area under the receiver operating characteristic curve (AUC). Furthermore, the accuracy could be increased to 100% and the AUC to 1.0 in a PCA-SVM model using only 13 carefully chosen features.