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.

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.

Introduction: Smart phones are becoming ubiquitous and their computing capabilities are ever increasing. Consequently, more attention is geared toward their potential use in research and medical settings. For instance, their built-in hardware can provide quantitative data for different movements. Therefore, the goal of the current study was to evaluate the capabilities of a standalone smart phone platform to characterize tremor. Results: Algorithms for tremor recording and online analysis can be implemented within a smart phone. The smart phone provides reliable time- and frequency-domain tremor characteristics. The smart phone can also provide medically relevant tremor assessments. Discussion: Smart phones have the potential to provide researchers and clinicians with quantitative short- and long-term tremor assessments that are currently not easily available. Methods: A smart phone application for tremor quantification and online analysis was developed. Then, smart phone results were compared to those obtained simultaneously with a laboratory accelerometer. Finally, results from the smart phone were compared to clinical tremor assessments.

Wearable sensors such as inertial measurement units (IMUs) have been widely used to measure the quantity of physical activities during daily living in healthy and people with movement disorders through activity classification. These sensors have the potential to provide valuable information to evaluate the quality of the movement during the activities of daily living (ADL), such as walking, sitting down, and standing up, which could help clinicians to monitor rehabilitation and pharmaceutical interventions. However, high accuracy in the detection and segmentation of these activities is necessary for proper evaluation of the quality of the performance within a given segment. This paper presents algorithms to process IMU data, to detect and segment unstructured ADL in people with Parkinson's disease (PD) in simulated free-living environment. The proposed method enabled the detection of 1610 events of ADL performed by nine community dwelling older adults with PD under simulated free-living environment with 90% accuracy (sensitivity = 90.8%, specificity = 97.8%) while segmenting these activities within 350 ms of the “gold-standard” manual segmentation. These results demonstrate the robustness of the proposed method to eventually be used to automatically detect and segment ADL in free-living environment in people with PD. This could potentially lead to a more expeditious evaluation of the quality of the movement and administration of proper corrective care for patients who are under physical rehabilitation and pharmaceutical intervention for movement disorders.

Wearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD). A modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms. Using the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms. This study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.

Joints kinematics assessment based on inertial measurement systems, which include attitude and heading reference system (AHRS), are quickly gaining in popularity for research and clinical applications. The variety of the tasks and contexts they are used in require a deep understanding of the AHRS accuracy for optimal data interpretation. However, published accuracy studies on AHRS are mostly limited to a single task measured on a limited number of segments and participants. This study assessed AHRS sensors kinematics accuracy at multiple segments and joints through a variety of tasks not only to characterize the system’s accuracy in these specific conditions, but also to extrapolate the accuracy results to a broader range of conditions using the characteristics of the movements (i.e. velocity and type of motion). Twenty asymptomatic adults ( $$\overline{age}$$ = 49.9) performed multiple 5 m timed up and go. Participants’ head, upper trunk, pelvis, thigh, shank and foot were simultaneously tracked using AHRS and an optical motion capture system (gold standard). Each trial was segmented into basic tasks (sit-to-stand, walk, turn). At segment level, results revealed a mean root-mean-squared-difference $$\overline{(RMSD)}$$ varying between 1.1° and 5.5° according to the segment tracked and the task performed, with a good to excellent agreement between the systems. Relative sensor kinematics accuracy (i.e. joint) varied between 1.6° and 13.6° over the same tasks. On a global scheme, analysis of the effect of velocity on sensor kinematics accuracy showed that AHRS are better adapted to motions performed between 50°/s and 75°/s (roughly thigh and shank while walking). Results confirmed that pairing of modules to obtain joint kinematics affects the accuracy compared to segment kinematics. Overall, AHRS are a suitable solution for clinical evaluation of biomechanics under the multi-segment tasks performed although the variation in accuracy should be taken into consideration when judging the clinical meaningfulness of the observed changes.