Background: Physical activity (PA) is increasingly advocated as an adjunct intervention for individuals with Parkinson’s disease (PD). However, the specific benefits of PA on the wide variety of impairments observed in patients with PD has yet to be clearly identified. Objective: Highlight health parameters that are most likely to improve as a result of PA interventions in patients with PD. Methods: We compiled results obtained from studies examining a PA intervention in patients with PD and who provided statistical analyses of their results. 868 outcome measures were extracted from 106 papers published from 1981 to 2015. The results were classified as having a statistically significant positive effect or no effect. Then, outcome measures were grouped into four main categories and further divided into sub-categories. Results: Our review shows that PA seems most effective in improving Physical capacities and Physical and cognitive functional capacities. On the other hand, PA seems less efficient at improving Clinical symptoms of PD and Psychosocial aspects of life, with only 50% or less of results reporting positive effects. The impact of PA on Cognitive functions and Depression also appears weaker, but few studies have examined these outcomes. Discussion: Our results indicate that PA interventions have a positive impact on physical capacities and functional capacities. However, the effect of PA on symptoms of the disease and psychosocial aspects of life are moderate and show more variability. This review also highlights the need for more research on the effects of PA on cognitive functions, depression as well as specific symptoms of PD.

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

Purpose: The purpose of this review is to evaluate the implementation and effectiveness of yoga for the reduction of symptoms of anxiety and depression in youth. To our knowledge, there are no systematic reviews to date looking at the reduction of symptoms of both anxiety and depression. Methods: Numerous scientific databases were searched up to November 2018 for experimental studies assessing changes in symptoms of anxiety and/or depression in youths following yoga interventions. Quality and level of evidence were assessed, and information was synthesized across studies. Results: Twenty-seven studies involving youth with varying health statuses were reviewed. Intervention characteristics varied greatly across studies revealing multiple factors that may impact intervention efficacy, however 70% of the studies overall showed improvements. For studies assessing anxiety and depression, 58% showed reductions in both symptoms, while 25% showed reductions in anxiety only. Additionally, 70% of studies assessing anxiety alone showed improvements and 40% of studies only assessing depression showed improvements. Conclusion: The studies reviewed, while of weak to moderate methodological quality, showed that yoga, defined by the practice of postures, generally leads to some reductions in anxiety and depression in youth regardless of health status and intervention characteristics.

This paper presents a novel model of tremor in Parkinson's disease (PD) based on extensive literature review as well as novel results stemming from functional stereotactic neurosurgery for the alleviation of tremor in PD. Specifically, evidence that suggests the basal ganglia induces PD tremor via excessive inhibitory output to the thalamus and altered firing patterns which in turn generate rhythmic bursting activity of thalamic cells is presented. Then, evidence that the thalamus generates PD tremor by facilitating the generation and consolidation of rhythmic bursting activity of neurons within its nuclei is also offered. Finally, evidence that the cerebellum may modulate characteristics of PD tremor by treating it as if it was a voluntary motor behavior is presented. Accordingly, the current paper proposes that PD tremor is induced by abnormal basal ganglia activity; it is generated by the thalamus, and modulated or reinforced by the cerebellum.

Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% (r2=0.70) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors’ rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.