Early balance impairment in Parkinson's Disease: Evidence from Robot-assisted axial rotations

Effectiveness of vestibular rehabilitation on postural balance in Parkinson's disease: a systematic review and meta-analysis of randomized controlled trials

INTRODUCTION

Postural balance impairment can affect the quality of life of patients with Parkinson's disease. Previous studies have described connections of the vestibular system with postural functions, suggesting a potential participation of the basal ganglia in receiving vestibular stimuli. This systematic review aims to summarize the evidence on the effectiveness of vestibular rehabilitation on postural balance in patients with Parkinson's disease.

METHODS

A systematic review was conducted using the electronic databases: PubMed, Embase, Scopus and PEDro. The study selection was independently conducted by two reviewers, and disagreements were evaluated by a third reviewer. The included studies had no restrictions on publication dates or languages and the last update occurred in July 2023.

RESULTS

From the 485 studies found in the searches, only 3 studies were deemed eligible for the systematic review involving a total of 130 participants. The Berg Balance Scale was described as the tool for evaluation of postural balance in all studies. The meta-analysis showed statistically significant results in favor of vestibular rehabilitation (MD = 5.35; 95% CI = 2.39, 8.31; P < 0.001), regardless of the stage of Parkinson's disease. Although the effect size was suggested as a useful functional gain, the analysis was done with caution, as it only included 3 randomized controlled trials. The risk of bias using the RoB-2 was considered as being of "some concern" in all studies. Furthermore, the quality of the evidence based on the Grading of Recommendations Assessment Development and Evaluation system, produced by pooling the included studies was considered very low.

CONCLUSION

Compared to other interventions, vestibular rehabilitation has potential to assist the postural balance of patients with Parkinson's disease. However, the very low quality of the evidence demonstrates uncertainty about the impact of this clinical practice. More robust studies are needed to confirm the benefits of this therapy in patients with Parkinson's disease. This study was prospectively registered in PROSPERO: CRD42020210185.

Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life

BACKGROUND

Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters.

METHODS

Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait.

RESULTS

The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage.

CONCLUSIONS

A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.

Effect of robot-assisted gait training on motor dysfunction in Parkinson's patients:A systematic review and meta-analysis

BACKGROUND

Robot-assisted gait training (RAGT) has been reported to treat motor dysfunction in patients with Parkinson's disease (PD) in the last few years. However, the benefits of RAGT for treating motor dysfunction in PD are still unclear.

OBJECTIVES

To investigate the efficacy of RAGT for motor dysfunction in PD patients.

METHODS

We searched PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang, Chinese Biomedical Literature Database (CBM), and Chinese VIP Database for randomized controlled trials investigating RAGT to improve motor dysfunction in PD from the databases' inception dates until September 1, 2022. The following outcome indexes were employed to evaluate motor dysfunction: the Berg Balance Scale (BBS), Activities-specific Balance Confidence Scale (ABC), 10-Meter Walk Test gait speed (10-MWT), gait speed, stride length, cadence Unified Parkinson Disease Rating Scale Part III (UPDRS III), 6-Minute Walk Test (6MWT), and the Timed Up and Go test (TUG). The meta-analysis was performed using the proper randomeffect model or fixed-effect model to evaluate the difference in efficacy between the RAGT and the control groups. The Cochrane Risk of Bias Tool was used for the included studies and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) was used to interpret the certainty of the results.

RESULTS

The results consisted of 17 studies comprising a total of 670 participants. Six hundred and seven PD patients with motor dysfunction were included: 335 in the RAGT group and 335 in the control group. This meta-analysis results established that when compared with the control group, robot-assisted gait training improved the BBS results of PD patients (MD: 2.80, 95%CI: 2.11-3.49, P< 0.00001), ABC score (MD: 7.30, 95%CI: 5.08-9.52, P< 0.00001), 10-MWT (MD: 0.06, 95%CI: 0.03-0.10, P= 0.0009), gait speed (MD: 3.67, 95%CI: 2.58-4.76, P< 0.00001), stride length (MD: 5.53, 95%CI: 3.64-7.42, P< 0.00001), cadence (MD: 4.52, 95%CI: 0.94-8.10, P= 0.01), UPDRS III (MD: -2.16, 95%CI: -2.48--1.83, P< 0.00001), 6MWT (MD: 13.87, 95%CI: 11.92-15.82, P< 0.00001). However, RAGT did not significantly improve the TUG test result of patients with PD (MD =-0.56, 95% CI: -1.12-0.00, P= 0.05). No safety concerns or adverse reactions among robot-assisted gait training patients were observed.

CONCLUSION

Even though RAGT can improve balance function, walking function, and gait performance and has demonstrated positive results in several studies, there is currently insufficient compelling evidence to suggest that it can improve all aspects of lower motor function.

Multi-muscle synergies in preparation for gait initiation in Parkinson's disease

OBJECTIVE

We investigated changes in indices of muscle synergies prior to gait initiation and the effects of gaze shift in patients with Parkinson's disease (PD). A long-term objective of the study is to develop a method for quantitative assessment of gait-initiation problems in PD.

METHODS

PD patients without clinical signs of postural instability and two control groups (age-matched and young) performed a gait initiation task in a self-paced manner, with and without a quick prior gaze shift produced by turning the head. Muscle groups with parallel scaling of activation levels (muscle modes) were identified as factors in the muscle activation space. Synergy index stabilizing center of pressure trajectory in the anterior-posterior and medio-lateral directions (indices of stability) was quantified in the muscle mode space. A drop in the synergy index in preparation to gait initiation (anticipatory synergy adjustment, ASA) was quantified.

RESULTS

Compared to the control groups, PD patients showed significantly smaller synergy indices and ASA for both directions of the center of pressure shift. Both PD and age-matched controls, but not younger controls, showed detrimental effects of the prior gaze shift on the ASA indices.

CONCLUSIONS

PD patients without clinically significant posture or gait disorders show impaired stability of the center of pressure and its diminished adjustment during gait initiation.

SIGNIFICANCE

The indices of stability and ASA may be useful to monitor pre-clinical gait disorders, and lower ASA may be relevant to emergence of freezing of gait in PD.

Evolution characteristics of dynamic balance disorder over the course of PD and relationship with dopamine depletion

Objective

This study aimed to assess the evolution of dynamic balance impairment during the course of Parkinson's disease (PD) and to clarify the contribution of striatal dopaminergic innervation to poor dynamic balance.

Methods

In our study, 89 patients with PD (divided into 2 groups according to the H-Y stage) and 39 controls were included. Kinematic data were recorded by a portable inertial measurement unit system. Dopaminergic loss in the striatal subregion was verified through the ¹¹C-CFT PET examination. The severity of white matter hyperintensities (WMHs) was assessed by the Scheltens scale. The correlation between dynamic kinematic parameters and dopamine transporter availability was analyzed by multivariate regression analysis.

Results

Patients with early PD presented with imbalance featured by smaller three-dimensional trunk ROM with reduced trunk coronal angular velocity during walking and with reduced trunk sagittal angular velocity during the stand-to-sit task (all p < 0.05). These abnormalities were not more severe at a later stage. The ROM in the coronal and transverse planes during walking correlated with caudate DAT uptake (β = 0.832, p = 0.006, Q = 0.030, and β = 0.890, p = 0.003, Q = 0.030) after controlling for age, gender, and WMHs. As the disease progressed, the trunk sagittal and transverse angular velocities during walking and trunk sagittal angular velocity when turning and sitting-to-standing were slower, which was accompanied by reduced gait velocity gradually (all p < 0.05). These parameters related to disease progression have no association with striatal DAT uptake (all p > 0.05).

Conclusion

The dynamic balance in PD was impaired from the early stages, and the characteristics of the impairment changed differently as the disease progressed. Dopaminergic denervation has a lower contribution to dynamic balance disorders throughout PD.

Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson's disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered "optimum" in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.

Feasibility and Application of the B.E.A.T. Testbed for Assessing the Effects of Lower Limb Exoskeletons on Human Balance

Assessing the performance of exoskeletons in assisting human balance is important for their design process. This study proposes a novel testbed, the B.E.A.T (Balance Evaluation Automated Testbed) to address this aim. We applied the B.E.A.T to evaluate how the presence of a lower limb exoskeleton influenced human balance. The B.E.A.T. consists of a robotic platform, standardized protocols, and performance indicators. Fifteen healthy subjects were enrolled and subjected to repeatable step-type ground perturbations in different directions using the multi-axis robotic platform. Each participant performed three trials, both with and without the exoskeleton (EXO and No-EXO conditions). Nine performance indicators, divided into kinematic and body stability indicators, were computed. The reliability of performance indicators was assessed by computing the Inter Class Correlation (ICC). The indicators showed good (0.60 ≤ ICC < 0.75) to excellent (ICC ≥ 0.75) reliability. The comparison between the EXO and No-EXO conditions revealed a significant increase in the joint range of motion and the center of pressure displacement while wearing the exoskeleton. The main differences between the EXO and No-EXO conditions were found in the range of motion of the knee joints, with an increment up to 17° in the sagittal plane.

Axial impairment and falls in Parkinson’s disease: 15 years of subthalamic deep brain stimulation

In this retrospective study, we longitudinally analyzed axial impairment and falls in people with Parkinson’s disease (PD) and subthalamic nucleus deep brain stimulation (STN-DBS). Axial scores and falling frequency were examined at baseline, and 1, 10, and 15 years after surgery. Preoperative demographic and clinical data, including PD duration and severity, phenotype, motor and cognitive scales, medications, and vascular changes on neuroimaging were examined as possible risk factors through Kaplan–Meier and Cox regression analyses. Of 302 individuals examined before and at 1 year after surgery, 102 and 57 were available also at 10 and 15 years of follow-up, respectively. Axial scores were similar at baseline and at 1 year but worsened at 10 and 15 years. The prevalence rate of frequent fallers progressively increased from baseline to 15 years. Preoperative axial scores, frontal dysfunction and age at PD onset were risk factors for axial impairment progression after surgery. Axial scores, akinetic/rigid phenotype, age at disease onset and disease duration at surgery predicted frequent falls. Overall, axial signs progressively worsened over the long-term period following STN-DBS, likely related to the progression of PD, especially in a subgroup of subjects with specific risk factors.

Clinical neurophysiology of Parkinson's disease and parkinsonism

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This review is part of the series on the clinical neurophysiology of movement disorders and focuses on Parkinson’s disease and parkinsonism.

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The pathophysiology of cardinal parkinsonian motor symptoms and myoclonus are reviewed.

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The recordings from microelectrode and deep brain stimulation electrodes are reported in detail.

This review is part of the series on the clinical neurophysiology of movement disorders. It focuses on Parkinson’s disease and parkinsonism. The topics covered include the pathophysiology of tremor, rigidity and bradykinesia, balance and gait disturbance and myoclonus in Parkinson’s disease. The use of electroencephalography, electromyography, long latency reflexes, cutaneous silent period, studies of cortical excitability with single and paired transcranial magnetic stimulation, studies of plasticity, intraoperative microelectrode recordings and recording of local field potentials from deep brain stimulation, and electrocorticography are also reviewed. In addition to advancing knowledge of pathophysiology, neurophysiological studies can be useful in refining the diagnosis, localization of surgical targets, and help to develop novel therapies for Parkinson’s disease.

Wearable Electrochemical Sensors in Parkinson's Disease

Parkinson’s disease (PD) is a neurodegenerative disorder associated with widespread aggregation of α-synuclein and dopaminergic neuronal loss in the substantia nigra pars compacta. As a result, striatal dopaminergic denervation leads to functional changes in the cortico-basal-ganglia-thalamo-cortical loop, which in turn cause most of the parkinsonian signs and symptoms. Despite tremendous advances in the field in the last two decades, the overall management (i.e., diagnosis and follow-up) of patients with PD remains largely based on clinical procedures. Accordingly, a relevant advance in the field would require the development of innovative biomarkers for PD. Recently, the development of miniaturized electrochemical sensors has opened new opportunities in the clinical management of PD thanks to wearable devices able to detect specific biological molecules from various body fluids. We here first summarize the main wearable electrochemical technologies currently available and their possible use as medical devices. Then, we critically discuss the possible strengths and weaknesses of wearable electrochemical devices in the management of chronic diseases including PD. Finally, we speculate about possible future applications of wearable electrochemical sensors in PD, such as the attractive opportunity for personalized closed-loop therapeutic approaches.

Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.