scholarly journals Implementation of the HiBalance training program for Parkinson’s disease in clinical settings: A feasibility study

2018 ◽  
Vol 8 (8) ◽  
pp. e01021 ◽  
Author(s):  
Conran Joseph ◽  
Breiffni Leavy ◽  
Sara Mattsson ◽  
Lynn Falk ◽  
Erika Franzén
2020 ◽  
Author(s):  
Zahra Rahmati ◽  
Saeed Behzadipour ◽  
Alfred C. Schouten ◽  
Ghorban Taghizadeh ◽  
Keikhosrow Firoozbakhsh

Abstract Background: Balance training improves postural control in Parkinson’s disease (PD). However, a systematic approach for the development of individualized, optimal training programs is still lacking, as the learning dynamics of the postural control in PD, over a training program are poorly understood. Objectives: We investigated the learning dynamics of the postural control in PD, during a balance-training program, in terms of the clinical, posturographic, and novel model-based measures. Methods: Twenty patients with PD participated in a balance-training program, 3 days a week, for 6 weeks. Clinical tests assessed functional balance and mobility pre-training, mid-training, and post-training. Center-of-pressure (COP) was recorded at four time-points during the training (pre-, week 2, week 4, and post-training). COP was used to calculate the sway measures and to identify the parameters of a patient-specific postural control model, at each time-point. The posturographic and model-based measures constituted the two sets of stability- and flexibility-related measures. Results: Mobility- and flexibility-related measures showed a continuous improvement during the balance-training program. In particular, mobility improved at mid-training and continued to improve to the end of the training, whereas flexibility-related measures reached significance only at the end. The progression in the balance- and stability-related measures was characterized by early improvements over the first three to four weeks of training, and reached a plateau for the rest of the training. Conclusions: The progression in balance and postural stability is achieved earlier and susceptible to plateau out, while mobility and flexibility continues to improve during the balance training.


2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Daniele Volpe ◽  
Matteo Signorini ◽  
Anna Marchetto ◽  
Timothy Lynch ◽  
Meg E Morris

Author(s):  
Pilar Fernández-González ◽  
María Carratalá-Tejada ◽  
Esther Monge-Pereira ◽  
Susana Collado-Vázquez ◽  
Patricia Sánchez-Herrera Baeza ◽  
...  

Abstract Background Non-immersive video games are currently being used as technological rehabilitation tools for individuals with Parkinson’s disease (PD). The aim of this feasibility study was to evaluate the effectiveness of the Leap Motion Controller® (LMC) system used with serious games designed for the upper limb (UL), as well as the levels of satisfaction and compliance among patients in mild-to-moderate stages of the disease. Methods A non-probabilistic sampling of non-consecutive cases was performed. 23 PD patients, in stages II-IV of the Hoehn & Yahr scale, were randomized into two groups: an experimental group (n = 12) who received treatment based on serious games designed by the research team using the LMC system for the UL, and a control group (n = 11) who received a specific intervention for the UL. Grip muscle strength, coordination, speed of movements, fine and gross UL dexterity, as well as satisfaction and compliance, were assessed in both groups pre-treatment and post-treatment. Results Within the experimental group, significant improvements were observed in all post-treatment assessments, except for Box and Blocks test for the less affected side. Clinical improvements were observed for all assessments in the control group. Statistical intergroup analysis showed significant improvements in coordination, speed of movements and fine motor dexterity scores on the more affected side of patients in the experimental group. Conclusions The LMC system and the serious games designed may be a feasible rehabilitation tool for the improvement of coordination, speed of movements and fine UL dexterity in PD patients. Further studies are needed to confirm these preliminary findings.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Amir Pourmoghaddam ◽  
Marius Dettmer ◽  
Daniel P. O’Connor ◽  
William H. Paloski ◽  
Charles S. Layne

Analysis of electromyographic (EMG) data is a cornerstone of research related to motor control in Parkinson’s disease. Nonlinear EMG analysis tools have shown to be valuable, but analysis is often complex and interpretation of the data may be difficult. A previously introduced algorithm (SYNERGOS) that provides a single index value based on simultaneous multiple muscle activations (MMA) has been shown to be effective in detecting changes in EMG activation due to modifications of walking speeds in healthy adults. In this study, we investigated if SYNERGOS detects MMA changes associated with both different walking speeds and levodopa intake. Nine male Parkinsonian patients walked on a treadmill with increasing speed while on or off medication. We collected EMG data and computed SYNERGOS indices and employed a restricted maximum likelihood linear mixed model to the values. SYNERGOS was sensitive to neuromuscular modifications due to both alterations of gait speed and intake of levodopa. We believe that the current experiment provides evidence for the potential value of SYNERGOS as a nonlinear tool in clinical settings, by providing a single value index of MMA. This could help clinicians to evaluate the efficacy of interventions and treatments in Parkinson’s disease in a simple manner.


2013 ◽  
Vol 19 (4) ◽  
pp. 298-304 ◽  
Author(s):  
Glenna A. Dowling ◽  
Robert Hone ◽  
Charles Brown ◽  
Judy Mastick ◽  
Marsha Melnick

2016 ◽  
Vol 32 (3) ◽  
pp. 773-780
Author(s):  
Ailyn Ferreira Souza ◽  
Sabrina Braço ◽  
Patricia Biagiotto ◽  
Marcelo Cesar Nonato da Silveira ◽  
Welton Ferreira de Assis ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Xue-ning Li ◽  
Da-peng Hao ◽  
Mei-jie Qu ◽  
Meng Zhang ◽  
An-bang Ma ◽  
...  

Background: Prediction and early diagnosis of Parkinson’s disease (PD) and Parkinson’s disease with depression (PDD) are essential for the clinical management of PD.Objectives: The present study aimed to develop a plasma Family with sequence similarity 19, member A5 (FAM19A5) and MRI-based radiomics nomogram to predict PD and PDD.Methods: The study involved 176 PD patients and 181 healthy controls (HC). Sandwich enzyme-linked immunosorbent assay (ELISA) was used to measure FAM19A5 concentration in the plasma samples collected from all participants. For enrolled subjects, MRI data were collected from 164 individuals (82 in the PD group and 82 in the HC group). The bilateral amygdala, head of the caudate nucleus, putamen, and substantia nigra, and red nucleus were manually labeled on the MR images. Radiomics features of the labeled regions were extracted. Further, machine learning methods were applied to shrink the feature size and build a predictive radiomics signature. The resulting radiomics signature was combined with plasma FAM19A5 concentration and other risk factors to establish logistic regression models for the prediction of PD and PDD.Results: The plasma FAM19A5 levels (2.456 ± 0.517) were recorded to be significantly higher in the PD group as compared to the HC group (2.23 ± 0.457) (P < 0.001). Importantly, the plasma FAM19A5 levels were also significantly higher in the PDD subgroup (2.577 ± 0.408) as compared to the non-depressive subgroup (2.406 ± 0.549) (P = 0.045 < 0.05). The model based on the combination of plasma FAM19A5 and radiomics signature showed excellent predictive validity for PD and PDD, with AUCs of 0.913 (95% CI: 0.861–0.955) and 0.937 (95% CI: 0.845–0.970), respectively.Conclusion: Altogether, the present study reported the development of nomograms incorporating radiomics signature, plasma FAM19A5, and clinical risk factors, which might serve as potential tools for early prediction of PD and PDD in clinical settings.


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