Physical activity based on dance movements as complementary therapy for Parkinson's disease: Effects on movement, executive functions, depressive symptoms, and quality of life.

BACKGROUND: Parkinson's disease (PD) is a progressive, neurodegenerative disease with motor symptoms that are well understood, but non-motor symptoms may be present and appear at different temporal stages of the disease. Physical activity based on dance movements is emerging as a complementary therapeutic approach to a range of PD symptoms as a multidimensional activity that requires rhythmic synchronization and more neuromuscular functions. OBJECTIVE: To evaluate the effects of physical activity based on dance movements on the movement, executive functions, depressive symptoms, quality of life, and severity of PD in individuals diagnosed with PD. METHODS: 13 individuals with PD (Hoehn & Yahr I-III, MDS-UPDRS 67.62 ± 20.83), underwent physical activity based on dance movements (2x week for 6 months). Participants were assessed at baseline and after 6 months on movement (POMA, TUG and MDS-UPDRS Part III), executive function (FAB), depressive symptoms (MADRS), quality of life (PDQ-39), and severity of PD (MDS-UPDRS TOTAL). Student's t-test was used to compare pre and post-intervention results. RESULTS: We observed a significant improvement in the movement (balance and gait) by the POMA test, p = 0.0207, executive function by the FAB test, p = 0.0074, abstract reasoning and inhibitory control by the FAB, Conceptualization test, p = 0.0062, and Inhibitory Control, p = 0.0064, depressive symptoms assessed by the MADRS test significantly reduced, p = 0.0214, and the quality of life by the PDQ-39 had a significant increase after the intervention, p = 0.0006, showed significant improvements between the pre-and post-intervention periods of physical activity based on dance movements. CONCLUSION: Physical activity based on dance movements contributed to significant improvements in movement (balance and gait), executive functions, especially in cognitive flexibility and inhibitory control, and the quality of life too. Sensorimotor integration, most cognitive processing and social skills may have contributed to the results. TRIAL REGISTRATION: The study was registered in the Brazilian registry of clinical trials: RBR-3bhbrb5.

Hand Resting Tremor Assessment of Healthy and Patients With Parkinson's Disease: An Exploratory Machine Learning Study.

The aim of this study is comparing the accuracies of machine learning algorithms to classify data concerning healthy subjects and patients with Parkinson's Disease (PD), toward different time window lengths and a number of features. Thirty-two healthy subjects and eighteen patients with PD took part on this study. The study obtained inertial recordings by using an accelerometer and a gyroscope assessing both hands of the subjects during hand resting state. We extracted time and temporal frequency domain features to feed seven machine learning algorithms: k-nearest-neighbors (kNN); logistic regression; support vector classifier (SVC); linear discriminant analysis; random forest; decision tree; and gaussian Naïve Bayes. The accuracy of the classifiers was compared using different numbers of extracted features (i.e., 272, 190, 136, 82, and 27) from different time window lengths (i.e., 1, 5, 10, and 15 s). The inertial recordings were characterized by oscillatory waveforms that, especially in patients with PD, peaked in a frequency range between 3 and 8 Hz. Outcomes showed that the most important features were the mean frequency, linear prediction coefficients, power ratio, power density skew, and kurtosis. We observed that accuracies calculated in the testing phase were higher than in the training phase. Comparing the testing accuracies, we found significant interactions among time window length and the type of classifier (p < 0.05). The study found significant effects on estimated accuracies, according to their type of algorithm, time window length, and their interaction. kNN presented the highest accuracy, while SVC showed the worst results. kNN feeding by features extracted from 1 and 5 s were the combination with more frequently highest accuracies. Classification using few features led to similar decision of the algorithms. Moreover, performance increased significantly according to the number of features used, reaching a plateau around 136. Finally, the results of this study suggested that kNN was the best algorithm to classify hand resting tremor in patients with PD.